Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
Global Descriptor Attributes Based Content Based Image Retrieval of Query ImagesIJERA Editor
The need for efficient content-based image retrieval system has increased hugely. Efficient and effective retrieval techniques of images are desired because of the explosive growth of digital images. Content based image retrieval (CBIR) is a promising approach because of its automatic indexing retrieval based on their semantic features and visual appearance. In this proposed system we investigate method for describing the contents of images which characterizes images by global descriptor attributes, where global features are extracted to make system more efficient by using color features which are color expectancy, color variance, skewness and texture feature correlation.
A Survey on Image Retrieval By Different Features and TechniquesIRJET Journal
This document discusses various techniques for content-based image retrieval. It begins with an introduction to content-based image retrieval and describes how it uses visual features like color, texture, shape and regions to index and represent image content for retrieval. The document then reviews related work on image retrieval using different features. It discusses features used for image identification like color, edges, corners and texture. The document also outlines techniques for image retrieval including relevance feedback, support vector machines, block truncation coding, and image clustering. Finally, it evaluates parameters for comparing image retrieval algorithms.
IRJET- Content Based Image Retrieval (CBIR)IRJET Journal
This document describes a content-based image retrieval system that uses color features to retrieve similar images from a large database. It discusses using color descriptor features to extract feature vectors from images that can then be used to retrieve near matches based on similarity. Color features provide approximate matches more quickly than individual approaches. The system works by extracting visual features from both a query image and images in the database, then comparing the features to retrieve the most similar matches from the database. Color histograms and color moments are discussed as common color features used for this type of content-based image retrieval.
Robust and Radial Image Comparison Using Reverse Image Search IJMER
This paper proposed a robust, radial and effective content-based image retrieval (CBIR)
or query by image content (QBIC) or content based visual information retrieval (CBVIR) approach,
which is based on colour, texture and shape features. Due to the enormous increase in image
database sizes, as well as its vast deployment in various applications, the need for CBIR development
arose. In this proposed approach, image attributes like image name, keywords and meta data are not
used to compute image similarity and image retrieval. So, concept based image retrieval is not used.
If an image is given as an input query and the output is based on the input image query, it is called as
reverse image search. So, images can be searched based on their contents (pixels) but not by their
keywords. It is difficult to measure image content similarity due to visual changes caused by varying
viewpoint and environment. In this paper, a simple and efficient method to effectively measure the
content similarity from image measurements is proposed. The proposed approach is based on the
three well-known algorithms: colour histogram, texture and moment invariants. It ensures that the
proposed image retrieval approach produces results which are highly relevant to the content of an
image query, by taking into account the three distinct features of the image and similarity metrics
based on Euclidean measure. Colour histogram is used to extract the colour features of an image.
Gabor filter is used to extract the texture features and the moment invariant is used to extract the
shape features of an image. It also uses fuzzy similarity measures.
CBIR Processing Approach on Colored and Texture Images using KNN Classifier a...IRJET Journal
This document presents a content-based image retrieval system that uses color and texture features. It uses a K-nearest neighbor classifier to classify images based on color features and extract texture features using log-Gabor filters. Images are then ranked based on their similarity to the query image using Spearman's rank correlation coefficient. The system is tested on a dataset of flag images to retrieve the most similar flags to a given query image based on color and texture features. Experimental results show that the combined approach of using classification, similarity measures and log-Gabor filtering for color and texture features provides better retrieval performance than methods using only wavelets or Gabor filters.
IRJET- Image based Information RetrievalIRJET Journal
This document discusses content-based image retrieval (CBIR) for retrieving images based on visual similarity. It focuses on using CBIR to match images of monuments for tourism applications. The paper describes extracting shape features using edge histogram descriptors to divide images into sub-images and compare edge distributions. An experiment matches images of Humayun's Tomb and the Statue of Liberty by comparing their edge magnitude values across sub-images. Similar edge distributions between two images' sub-images indicates similarity in shape and matches the images. The paper concludes CBIR using shape features can effectively match similar images of monuments to provide relevant information to users.
A Hybrid Approach for Content Based Image Retrieval SystemIOSR Journals
This document describes a hybrid approach for content-based image retrieval. It combines several spatial features - row sum, column sum, forward and backward diagonal sums - and histograms to represent images with feature vectors. Euclidean distance is used to calculate similarity between a query image's feature vector and those in the database. The approach is evaluated using precision-recall calculations on different image groups, showing the hybrid method performs best by combining multiple features.
Retrieval of Images Using Color, Shape and Texture Features Based on Contentrahulmonikasharma
The current study deals with deriving of image feature descriptor by error diffusion based block truncation coding (EDBTC). The image feature descriptor is basically comprised by the two error diffusion block truncation coding, color quantizers and its equivalent bitmap image. The bitmap image distinguish the image edges and textural information of two color quantizers to signify the color allocation and image contrast derived by the Bit Pattern Feature and Color Co-occurrence Feature. Tentative outcome reveal the benefit of proposed feature descriptor as contrast to existing schemes in image retrieval assignment under normal and textural images. The Error-Diffusion Block Truncation Coding method compresses an image efficiently, and at the same time, its consequent compacted information flow can provides an efficient feature descriptor intended for operating image recovery and categorization. As a result, the proposed design preserves an effective candidate for real-time image retrieval applications.
Global Descriptor Attributes Based Content Based Image Retrieval of Query ImagesIJERA Editor
The need for efficient content-based image retrieval system has increased hugely. Efficient and effective retrieval techniques of images are desired because of the explosive growth of digital images. Content based image retrieval (CBIR) is a promising approach because of its automatic indexing retrieval based on their semantic features and visual appearance. In this proposed system we investigate method for describing the contents of images which characterizes images by global descriptor attributes, where global features are extracted to make system more efficient by using color features which are color expectancy, color variance, skewness and texture feature correlation.
A Survey on Image Retrieval By Different Features and TechniquesIRJET Journal
This document discusses various techniques for content-based image retrieval. It begins with an introduction to content-based image retrieval and describes how it uses visual features like color, texture, shape and regions to index and represent image content for retrieval. The document then reviews related work on image retrieval using different features. It discusses features used for image identification like color, edges, corners and texture. The document also outlines techniques for image retrieval including relevance feedback, support vector machines, block truncation coding, and image clustering. Finally, it evaluates parameters for comparing image retrieval algorithms.
IRJET- Content Based Image Retrieval (CBIR)IRJET Journal
This document describes a content-based image retrieval system that uses color features to retrieve similar images from a large database. It discusses using color descriptor features to extract feature vectors from images that can then be used to retrieve near matches based on similarity. Color features provide approximate matches more quickly than individual approaches. The system works by extracting visual features from both a query image and images in the database, then comparing the features to retrieve the most similar matches from the database. Color histograms and color moments are discussed as common color features used for this type of content-based image retrieval.
Robust and Radial Image Comparison Using Reverse Image Search IJMER
This paper proposed a robust, radial and effective content-based image retrieval (CBIR)
or query by image content (QBIC) or content based visual information retrieval (CBVIR) approach,
which is based on colour, texture and shape features. Due to the enormous increase in image
database sizes, as well as its vast deployment in various applications, the need for CBIR development
arose. In this proposed approach, image attributes like image name, keywords and meta data are not
used to compute image similarity and image retrieval. So, concept based image retrieval is not used.
If an image is given as an input query and the output is based on the input image query, it is called as
reverse image search. So, images can be searched based on their contents (pixels) but not by their
keywords. It is difficult to measure image content similarity due to visual changes caused by varying
viewpoint and environment. In this paper, a simple and efficient method to effectively measure the
content similarity from image measurements is proposed. The proposed approach is based on the
three well-known algorithms: colour histogram, texture and moment invariants. It ensures that the
proposed image retrieval approach produces results which are highly relevant to the content of an
image query, by taking into account the three distinct features of the image and similarity metrics
based on Euclidean measure. Colour histogram is used to extract the colour features of an image.
Gabor filter is used to extract the texture features and the moment invariant is used to extract the
shape features of an image. It also uses fuzzy similarity measures.
CBIR Processing Approach on Colored and Texture Images using KNN Classifier a...IRJET Journal
This document presents a content-based image retrieval system that uses color and texture features. It uses a K-nearest neighbor classifier to classify images based on color features and extract texture features using log-Gabor filters. Images are then ranked based on their similarity to the query image using Spearman's rank correlation coefficient. The system is tested on a dataset of flag images to retrieve the most similar flags to a given query image based on color and texture features. Experimental results show that the combined approach of using classification, similarity measures and log-Gabor filtering for color and texture features provides better retrieval performance than methods using only wavelets or Gabor filters.
IRJET- Image based Information RetrievalIRJET Journal
This document discusses content-based image retrieval (CBIR) for retrieving images based on visual similarity. It focuses on using CBIR to match images of monuments for tourism applications. The paper describes extracting shape features using edge histogram descriptors to divide images into sub-images and compare edge distributions. An experiment matches images of Humayun's Tomb and the Statue of Liberty by comparing their edge magnitude values across sub-images. Similar edge distributions between two images' sub-images indicates similarity in shape and matches the images. The paper concludes CBIR using shape features can effectively match similar images of monuments to provide relevant information to users.
A Hybrid Approach for Content Based Image Retrieval SystemIOSR Journals
This document describes a hybrid approach for content-based image retrieval. It combines several spatial features - row sum, column sum, forward and backward diagonal sums - and histograms to represent images with feature vectors. Euclidean distance is used to calculate similarity between a query image's feature vector and those in the database. The approach is evaluated using precision-recall calculations on different image groups, showing the hybrid method performs best by combining multiple features.
Retrieval of Images Using Color, Shape and Texture Features Based on Contentrahulmonikasharma
The current study deals with deriving of image feature descriptor by error diffusion based block truncation coding (EDBTC). The image feature descriptor is basically comprised by the two error diffusion block truncation coding, color quantizers and its equivalent bitmap image. The bitmap image distinguish the image edges and textural information of two color quantizers to signify the color allocation and image contrast derived by the Bit Pattern Feature and Color Co-occurrence Feature. Tentative outcome reveal the benefit of proposed feature descriptor as contrast to existing schemes in image retrieval assignment under normal and textural images. The Error-Diffusion Block Truncation Coding method compresses an image efficiently, and at the same time, its consequent compacted information flow can provides an efficient feature descriptor intended for operating image recovery and categorization. As a result, the proposed design preserves an effective candidate for real-time image retrieval applications.
A hybrid content based image retrieval system using log-gabor filter banksIJECEIAES
In this paper, a new efficient image retrieval system using sequential process of three stages with filtering technique for the feature selection is proposed. In the first stage the color features are extracted using color histogram method and in the second stage the texture features are obtained using log-Gabor filters and in the third stage shape features are extracted using shape descriptors using polygonal fitting algorithm. The proposed log-Gabor filter in the second stage has advantages of retrieving images over regular Gabor filter for texture. It provides better representation of the images. Experimental evaluation of the proposed system shows improved performance in retrieval as compared to other existing systems in terms of average precision and average recall.
A Novel Method for Content Based Image Retrieval using Local Features and SVM...IRJET Journal
1) The document presents a novel approach for content-based image retrieval that uses local features like color, texture, and edges extracted from images.
2) It extracts these features and uses an SVM classifier to optimize retrieval results. This improves accuracy compared to other techniques that use only one content feature.
3) The proposed system is tested on parameters like accuracy, sensitivity, specificity, error rate, and retrieval time, and shows better performance than other methods.
This document presents a content-based image retrieval semantic model for shaped and unshaped objects. It proposes classifying objects into two categories: shaped objects with a fixed shape like animals and objects, and unshaped objects without a fixed shape like landscapes. For unshaped objects, local regions are classified by frequency of occurrence and semantic concepts are evaluated using color, shape, and regional dissimilarity factors. For shaped objects, semantic concepts are measured using normalized color, edge detection, particle removal, and shape similarity. Several existing content-based image retrieval techniques are also briefly discussed.
Image search using similarity measures based on circular sectorscsandit
With growing number of stored image data, image sea
rch and image similarity problem become
more and more important. The answer can be solved b
y Content-Based Image Retrieval
systems. This paper deals with an image search usin
g similarity measures based on circular
sectors method. The method is inspired by human eye
functionality. The main contribution of the
paper is a modified method that increases accuracy
for about 8% in comparison with original
approach. Here proposed method has used HSB colour
model and median function for feature
extraction. The original approach uses RGB colour m
odel with mean function. Implemented
method was validated on 10 image categories where o
verall average precision was 67%
A Review of Feature Extraction Techniques for CBIR based on SVMIJEEE
As with the advancement of multimedia technologies, users are not gratified with the conventional retrieval system techniques. So a application “Content Based Image Retrieval System” is introduced. CBIR is the application to retrieve the images or to search the digital images from the large database .The term “content” deals with the colour, shape, texture and all the information which is extracted from the image itself. This paper reviews the CBIR system which uses SVM classifier based algorithms for feature extraction phase.
A Comparative Study of Content Based Image Retrieval Trends and ApproachesCSCJournals
Content Based Image Retrieval (CBIR) is an important step in addressing image storage and management problems. Latest image technology improvements along with the Internet growth have led to a huge amount of digital multimedia during the recent decades. Various methods, algorithms and systems have been proposed to solve these problems. Such studies revealed the indexing and retrieval concepts, which have further evolved to Content-Based Image Retrieval. CBIR systems often analyze image content via the so-called low-level features for indexing and retrieval, such as color, texture and shape. In order to achieve significantly higher semantic performance, recent systems seek to combine low-level with high-level features that contain perceptual information for human. Purpose of this review is to identify the set of methods that have been used for CBR and also to discuss some of the key contributions in the current decade related to image retrieval and main challenges involved in the adaptation of existing image retrieval techniques to build useful systems that can handle real-world data. By making use of various CBIR approaches accurate, repeatable, quantitative data must be efficiently extracted in order to improve the retrieval accuracy of content-based image retrieval systems. In this paper, various approaches of CBIR and available algorithms are reviewed. Comparative results of various techniques are presented and their advantages, disadvantages and limitations are discussed.
Department, Bharati Vidyapeeth’s College of Engineering for Women, Maharashtra, India
Abstract
With the wide - spread use of image retrieval in various areas such as crime investigation, medical diagnosis, intellectual
property rights, etc, today’s need is to enhance the image retrieval process. In our research, we are combining Text Based Image
Retrieval (TBIR) method with Content Based Image Retrieval (CBIR) method to enhance image retrieval. The base of CBIR is to
extract different image features, such as Color, Shape and Texture. To improve the accuracy, we are using combination of most
efficient feature extraction algorithms. We are using RGB to Lab conversion for color feature extraction, Modified Canny edge
detection algorithm with variable sigma for shape feature extraction, Framelet transform method for texture feature extraction.
For improving the speed of image retrieval process using TBIR, we are implementing automatic annotation technique. Images are
annotated automatically without human intervention. It improves speed. Approximately one to two thousand images are stored in
the database. Features are extracted from these images and stored into the database. Query images are processed in the similar
way and similarity matching between query and database images is done through Hybrid Graph method. For that purpose, we
have to generate image to image graph from extracted feature vectors and image to tag graph from database. Combining both
these graphs, we get the Hybrid graph. Thus, the process of image retrieval is becoming efficient in both terms accuracy and time.
Also, user can give input in terms of query image or textual query or sketch. This improves human – friendliness of this system.
Keywords: feature extraction, Lab, Modified Canny detection, Framelet transform, automatic annotation, similarity
matching, Hybrid Graph, etc.
MMFO: modified moth flame optimization algorithm for region based RGB color i...IJECEIAES
Region-based color image segmentation is elementary steps in image processing and computer vision. The region-based color image segmentation has faced the problem of multidimensionality. The color image is considered in five-dimensional problems, in which three dimensions in color (RGB) and two dimensions in geometry (luminosity layer and chromaticity layer). In this paper, L*a*b color space conversion has been used to reduce the one dimension and geometrically it converts in the array hence the further one dimension has been reduced. This paper introduced, an improved algorithm modified moth flame optimization (MMFO) algorithm for RGB color image segmentation which is based on bio-inspired techniques. The simulation results of MMFO for region based color image segmentation are performed better as compared to PSO and GA, in terms of computation times for all the images. The experiment results of this method gives clear segments based on the different color and the different number of clusters is used during the segmentation process.
Content Based Image Retrieval : Classification Using Neural Networksijma
In a content-based image retrieval system (CBIR), the main issue is to extract the image features that
effectively represent the image contents in a database. Such an extraction requires a detailed evaluation of
retrieval performance of image features. This paper presents a review of fundamental aspects of content
based image retrieval including feature extraction of color and texture features. Commonly used color
features including color moments, color histogram and color correlogram and Gabor texture are
compared. The paper reviews the increase in efficiency of image retrieval when the color and texture
features are combined. The similarity measures based on which matches are made and images are
retrieved are also discussed. For effective indexing and fast searching of images based on visual features,
neural network based pattern learning can be used to achieve effective classification.
This document discusses content-based image retrieval (CBIR) using interactive genetic algorithms. It proposes using IGA to better capture a user's image preferences through iterative refinement of feature weights. Features discussed include color (mean and standard deviation in HSV color space), texture (entropy based on gray level co-occurrence matrix), and edges. IGA allows users to provide feedback on retrieval results to gradually shape the algorithm toward their interests over multiple generations. The document reviews related work using other features for CBIR and discusses color, texture, and edge features in more detail.
Retrieval of Monuments Images Through ACO Optimization ApproachIRJET Journal
This document presents a content-based image retrieval system for retrieving monument images using ant colony optimization for feature selection. It extracts low-level features including shape, texture, and color from images. Shape feature is extracted using morphological gradients. Texture feature uses an improved local binary pattern method. Color feature uses color moments. Ant colony optimization is then used to select the most relevant features. This approach aims to make it easier to identify and retrieve similar monument images from a database.
An Unsupervised Cluster-based Image Retrieval Algorithm using Relevance FeedbackIJMIT JOURNAL
Content-based image retrieval (CBIR) systems utilize low level query image feature as identifying similarity between a query image and the image database. Image contents are plays significant role for image retrieval. There are three fundamental bases for content-based image retrieval, i.e. visual feature extraction, multidimensional indexing, and retrieval system design. Each image has three contents such as: color, texture and shape features. Color and texture both plays important image visual features used in Content-Based Image Retrieval to improve results. Color histogram and texture features have potential to retrieve similar images on the basis of their properties. As the feature extracted from a query is low level, it is extremely difficult for user to provide an appropriate example in based query. To overcome these problems and reach higher accuracy in CBIR system, providing user with relevance feedback is famous for provide promising solutio
Low level features for image retrieval basedcaijjournal
In this paper, we present a novel approach for image retrieval based on extraction of low level features
using techniques such as Directional Binary Code (DBC), Haar Wavelet transform and Histogram of
Oriented Gradients (HOG). The DBC texture descriptor captures the spatial relationship between any pair
of neighbourhood pixels in a local region along a given direction, while Local Binary Patterns (LBP)
descriptor considers the relationship between a given pixel and its surrounding neighbours. Therefore,
DBC captures more spatial information than LBP and its variants, also it can extract more edge
information than LBP. Hence, we employ DBC technique in order to extract grey level texture features
(texture map) from each RGB channels individually and computed texture maps are further combined
which represents colour texture features (colour texture map) of an image. Then, we decomposed the
extracted colour texture map and original image using Haar wavelet transform. Finally, we encode the
shape and local features of wavelet transformed images using Histogram of Oriented Gradients (HOG) for
content based image retrieval. The performance of proposed method is compared with existing methods on
two databases such as Wang’s corel image and Caltech 256. The evaluation results show that our
approach outperforms the existing methods for image retrieval.
This document discusses content-based image mining techniques for image retrieval. It provides an overview of image mining, describing how image mining goes beyond content-based image retrieval by aiming to discover significant patterns in large image collections according to user queries. The document reviews several existing image mining techniques, including those using color histograms, texture analysis, clustering algorithms like k-means, and association rule mining. It discusses challenges in developing universal image retrieval methods and proposes combining low-level visual features with high-level semantic features. Overall, the document surveys the state of the art in content-based image mining and retrieval.
In this project, we proposed a Content Based Image Retrieval (CBIR) system which is used to retrieve a
relevant image from an outsized database. Textile images showed the way for the development of CBIR. It
establishes the efficient combination of color, shape and texture features. Here the textile image is given as
dataset. The images in database are loaded. The resultant image is given as input to feature extraction
technique which is transformation of input image into a set of features such as color, texture and shape.
The texture feature of an image is taken out by using Gray level co-occurrence matrix (GLCM). The color
feature of an image is obtained by HSI color space. The shape feature of an image is extorted by sobel
technique. These algorithms are used to calculate the similarity between extracted features. These features
are combined effectively so that the retrieval accuracy and recall rate is enhanced. The classification
techniques such as Support Vector Machine (SVM) are used to classify the features of a query image by
splitting the group such as color, shape and texture. Finally, the relevant images are retrieved from a large
database and hence the efficiency of an image is plotted.The software used is MATLAB 7.10 (matrix
laboratory) which is built software applications
C OMPARATIVE S TUDY OF D IMENSIONALITY R EDUCTION T ECHNIQUES U SING PCA AND ...csandit
The aim of this paper is to present a comparative s
tudy of two linear dimension reduction
methods namely PCA (Principal Component Analysis) a
nd LDA (Linear Discriminant Analysis).
The main idea of PCA is to transform the high dimen
sional input space onto the feature space
where the maximal variance is displayed. The featur
e selection in traditional LDA is obtained
by maximizing the difference between classes and mi
nimizing the distance within classes. PCA
finds the axes with maximum variance for the whole
data set where LDA tries to find the axes
for best class seperability. The proposed method is
experimented over a general image database
using Matlab. The performance of these systems has
been evaluated by Precision and Recall
measures. Experimental results show that PCA based
dimension reduction method gives the
better performance in terms of higher precision and
recall values with lesser computational
complexity than the LDA based method.
Web Image Retrieval Using Visual Dictionaryijwscjournal
In this research, we have proposed semantic based image retrieval system to retrieve set of relevant images for the given query image from the Web. We have used global color space model and Dense SIFT feature extraction technique to generate visual dictionary using proposed quantization algorithm. The images are transformed into set of features. These features are used as inputs in our proposed Quantization algorithm for generating the code word to form visual dictionary. These codewords are used to represent images semantically to form visual labels using Bag-of-Features (BoF). The Histogram intersection method is used to measure the distance between input image and the set of images in the image database to retrieve similar images. The experimental results are evaluated over a collection of 1000 generic Web images to demonstrate the effectiveness of the proposed system.
This document summarizes and reviews several techniques for image mining, including feature extraction, image clustering, and object recognition algorithms. It discusses color, texture, and edge feature extraction techniques and evaluates their precision and recall. It also describes the block truncation algorithm for image recognition and the cascade feature extraction approach. The key techniques - color moments, block truncation coding, and cascade classifiers - are evaluated based on experimental recall and precision results. Overall, the document provides an overview of different image mining techniques and evaluates their effectiveness.
Content Based Image and Video Retrieval AlgorithmAkshit Bum
The document describes content-based image and video retrieval (CBIR) algorithms. It discusses how CBIR works by extracting features from query images, indexing images, and retrieving similar images based on color, shape, and texture features. CBIR techniques include reverse image search, semantic retrieval using queries, and relevance feedback to refine searches based on user input about retrieved images. The document provides examples of CBIR applications in areas like crime prevention, military, web searching, and medical diagnosis.
A Study on Image Retrieval Features and Techniques with Various CombinationsIRJET Journal
This document discusses image retrieval techniques for content-based image retrieval systems. It begins with an introduction to the growth of digital image collections and the need for large-scale image retrieval systems. It then reviews different features used for image retrieval, such as color histograms, color moments, color coherence vectors, and discrete wavelet transforms. Edge features and corner features are also discussed. The document concludes that using only one feature type such as color or texture is not sufficient, and the best approach is to extract multiple high-quality features and combine them for image retrieval.
During past few years, people have been substantially attracted towards Content-Based Image Retrieval (CBIR) because of its varied multimedia applications. CBIR is one of the most popular research areas of digital image processing. The goal of the CBIR is to extract the visual features of the image such as color, texture or shape. An attempt is made to develop a Sketch Based Image Retrieval (SBIR) making it use of the features such as shape or form of the object [1]. This paper aims to introduce the creation and design of SBIR system making use of extraction techniques of Biased Maximum Margin Analysis (BMMA) and a Semi-Supervised Biased Maximum Margin Analysis (Semi BMMA) [2].. With the help of existing methods, design a task specific descriptor, which can handle the informational gap between a sketch and a colored image. The result of SBIR includes the set of positive images (relevant) and negative images (irrelevant). Iterative process acts on the positive set for the optimal extraction of the object. By using Laplacian regularizer to BMMA, the Semi BMMA integrates the information of unlabelled samples [2] to result in the better extraction of refined set of objects. The SBIR have several applications such as digital libraries, crime prevention, photo sharing sites etc. An important application is a matching a forensic image to gallery of mug shot images.
A SURVEY ON CONTENT BASED IMAGE RETRIEVAL USING MACHINE LEARNINGIRJET Journal
This document provides a literature review of recent research on content-based image retrieval using machine learning techniques. It summarizes 8 research papers that used approaches like convolutional neural networks, color histograms, deep learning, hashing functions and more to extract image features and retrieve similar images from databases. The goal of content-based image retrieval is to find images that are semantically similar to a query image based on visual features.
A hybrid content based image retrieval system using log-gabor filter banksIJECEIAES
In this paper, a new efficient image retrieval system using sequential process of three stages with filtering technique for the feature selection is proposed. In the first stage the color features are extracted using color histogram method and in the second stage the texture features are obtained using log-Gabor filters and in the third stage shape features are extracted using shape descriptors using polygonal fitting algorithm. The proposed log-Gabor filter in the second stage has advantages of retrieving images over regular Gabor filter for texture. It provides better representation of the images. Experimental evaluation of the proposed system shows improved performance in retrieval as compared to other existing systems in terms of average precision and average recall.
A Novel Method for Content Based Image Retrieval using Local Features and SVM...IRJET Journal
1) The document presents a novel approach for content-based image retrieval that uses local features like color, texture, and edges extracted from images.
2) It extracts these features and uses an SVM classifier to optimize retrieval results. This improves accuracy compared to other techniques that use only one content feature.
3) The proposed system is tested on parameters like accuracy, sensitivity, specificity, error rate, and retrieval time, and shows better performance than other methods.
This document presents a content-based image retrieval semantic model for shaped and unshaped objects. It proposes classifying objects into two categories: shaped objects with a fixed shape like animals and objects, and unshaped objects without a fixed shape like landscapes. For unshaped objects, local regions are classified by frequency of occurrence and semantic concepts are evaluated using color, shape, and regional dissimilarity factors. For shaped objects, semantic concepts are measured using normalized color, edge detection, particle removal, and shape similarity. Several existing content-based image retrieval techniques are also briefly discussed.
Image search using similarity measures based on circular sectorscsandit
With growing number of stored image data, image sea
rch and image similarity problem become
more and more important. The answer can be solved b
y Content-Based Image Retrieval
systems. This paper deals with an image search usin
g similarity measures based on circular
sectors method. The method is inspired by human eye
functionality. The main contribution of the
paper is a modified method that increases accuracy
for about 8% in comparison with original
approach. Here proposed method has used HSB colour
model and median function for feature
extraction. The original approach uses RGB colour m
odel with mean function. Implemented
method was validated on 10 image categories where o
verall average precision was 67%
A Review of Feature Extraction Techniques for CBIR based on SVMIJEEE
As with the advancement of multimedia technologies, users are not gratified with the conventional retrieval system techniques. So a application “Content Based Image Retrieval System” is introduced. CBIR is the application to retrieve the images or to search the digital images from the large database .The term “content” deals with the colour, shape, texture and all the information which is extracted from the image itself. This paper reviews the CBIR system which uses SVM classifier based algorithms for feature extraction phase.
A Comparative Study of Content Based Image Retrieval Trends and ApproachesCSCJournals
Content Based Image Retrieval (CBIR) is an important step in addressing image storage and management problems. Latest image technology improvements along with the Internet growth have led to a huge amount of digital multimedia during the recent decades. Various methods, algorithms and systems have been proposed to solve these problems. Such studies revealed the indexing and retrieval concepts, which have further evolved to Content-Based Image Retrieval. CBIR systems often analyze image content via the so-called low-level features for indexing and retrieval, such as color, texture and shape. In order to achieve significantly higher semantic performance, recent systems seek to combine low-level with high-level features that contain perceptual information for human. Purpose of this review is to identify the set of methods that have been used for CBR and also to discuss some of the key contributions in the current decade related to image retrieval and main challenges involved in the adaptation of existing image retrieval techniques to build useful systems that can handle real-world data. By making use of various CBIR approaches accurate, repeatable, quantitative data must be efficiently extracted in order to improve the retrieval accuracy of content-based image retrieval systems. In this paper, various approaches of CBIR and available algorithms are reviewed. Comparative results of various techniques are presented and their advantages, disadvantages and limitations are discussed.
Department, Bharati Vidyapeeth’s College of Engineering for Women, Maharashtra, India
Abstract
With the wide - spread use of image retrieval in various areas such as crime investigation, medical diagnosis, intellectual
property rights, etc, today’s need is to enhance the image retrieval process. In our research, we are combining Text Based Image
Retrieval (TBIR) method with Content Based Image Retrieval (CBIR) method to enhance image retrieval. The base of CBIR is to
extract different image features, such as Color, Shape and Texture. To improve the accuracy, we are using combination of most
efficient feature extraction algorithms. We are using RGB to Lab conversion for color feature extraction, Modified Canny edge
detection algorithm with variable sigma for shape feature extraction, Framelet transform method for texture feature extraction.
For improving the speed of image retrieval process using TBIR, we are implementing automatic annotation technique. Images are
annotated automatically without human intervention. It improves speed. Approximately one to two thousand images are stored in
the database. Features are extracted from these images and stored into the database. Query images are processed in the similar
way and similarity matching between query and database images is done through Hybrid Graph method. For that purpose, we
have to generate image to image graph from extracted feature vectors and image to tag graph from database. Combining both
these graphs, we get the Hybrid graph. Thus, the process of image retrieval is becoming efficient in both terms accuracy and time.
Also, user can give input in terms of query image or textual query or sketch. This improves human – friendliness of this system.
Keywords: feature extraction, Lab, Modified Canny detection, Framelet transform, automatic annotation, similarity
matching, Hybrid Graph, etc.
MMFO: modified moth flame optimization algorithm for region based RGB color i...IJECEIAES
Region-based color image segmentation is elementary steps in image processing and computer vision. The region-based color image segmentation has faced the problem of multidimensionality. The color image is considered in five-dimensional problems, in which three dimensions in color (RGB) and two dimensions in geometry (luminosity layer and chromaticity layer). In this paper, L*a*b color space conversion has been used to reduce the one dimension and geometrically it converts in the array hence the further one dimension has been reduced. This paper introduced, an improved algorithm modified moth flame optimization (MMFO) algorithm for RGB color image segmentation which is based on bio-inspired techniques. The simulation results of MMFO for region based color image segmentation are performed better as compared to PSO and GA, in terms of computation times for all the images. The experiment results of this method gives clear segments based on the different color and the different number of clusters is used during the segmentation process.
Content Based Image Retrieval : Classification Using Neural Networksijma
In a content-based image retrieval system (CBIR), the main issue is to extract the image features that
effectively represent the image contents in a database. Such an extraction requires a detailed evaluation of
retrieval performance of image features. This paper presents a review of fundamental aspects of content
based image retrieval including feature extraction of color and texture features. Commonly used color
features including color moments, color histogram and color correlogram and Gabor texture are
compared. The paper reviews the increase in efficiency of image retrieval when the color and texture
features are combined. The similarity measures based on which matches are made and images are
retrieved are also discussed. For effective indexing and fast searching of images based on visual features,
neural network based pattern learning can be used to achieve effective classification.
This document discusses content-based image retrieval (CBIR) using interactive genetic algorithms. It proposes using IGA to better capture a user's image preferences through iterative refinement of feature weights. Features discussed include color (mean and standard deviation in HSV color space), texture (entropy based on gray level co-occurrence matrix), and edges. IGA allows users to provide feedback on retrieval results to gradually shape the algorithm toward their interests over multiple generations. The document reviews related work using other features for CBIR and discusses color, texture, and edge features in more detail.
Retrieval of Monuments Images Through ACO Optimization ApproachIRJET Journal
This document presents a content-based image retrieval system for retrieving monument images using ant colony optimization for feature selection. It extracts low-level features including shape, texture, and color from images. Shape feature is extracted using morphological gradients. Texture feature uses an improved local binary pattern method. Color feature uses color moments. Ant colony optimization is then used to select the most relevant features. This approach aims to make it easier to identify and retrieve similar monument images from a database.
An Unsupervised Cluster-based Image Retrieval Algorithm using Relevance FeedbackIJMIT JOURNAL
Content-based image retrieval (CBIR) systems utilize low level query image feature as identifying similarity between a query image and the image database. Image contents are plays significant role for image retrieval. There are three fundamental bases for content-based image retrieval, i.e. visual feature extraction, multidimensional indexing, and retrieval system design. Each image has three contents such as: color, texture and shape features. Color and texture both plays important image visual features used in Content-Based Image Retrieval to improve results. Color histogram and texture features have potential to retrieve similar images on the basis of their properties. As the feature extracted from a query is low level, it is extremely difficult for user to provide an appropriate example in based query. To overcome these problems and reach higher accuracy in CBIR system, providing user with relevance feedback is famous for provide promising solutio
Low level features for image retrieval basedcaijjournal
In this paper, we present a novel approach for image retrieval based on extraction of low level features
using techniques such as Directional Binary Code (DBC), Haar Wavelet transform and Histogram of
Oriented Gradients (HOG). The DBC texture descriptor captures the spatial relationship between any pair
of neighbourhood pixels in a local region along a given direction, while Local Binary Patterns (LBP)
descriptor considers the relationship between a given pixel and its surrounding neighbours. Therefore,
DBC captures more spatial information than LBP and its variants, also it can extract more edge
information than LBP. Hence, we employ DBC technique in order to extract grey level texture features
(texture map) from each RGB channels individually and computed texture maps are further combined
which represents colour texture features (colour texture map) of an image. Then, we decomposed the
extracted colour texture map and original image using Haar wavelet transform. Finally, we encode the
shape and local features of wavelet transformed images using Histogram of Oriented Gradients (HOG) for
content based image retrieval. The performance of proposed method is compared with existing methods on
two databases such as Wang’s corel image and Caltech 256. The evaluation results show that our
approach outperforms the existing methods for image retrieval.
This document discusses content-based image mining techniques for image retrieval. It provides an overview of image mining, describing how image mining goes beyond content-based image retrieval by aiming to discover significant patterns in large image collections according to user queries. The document reviews several existing image mining techniques, including those using color histograms, texture analysis, clustering algorithms like k-means, and association rule mining. It discusses challenges in developing universal image retrieval methods and proposes combining low-level visual features with high-level semantic features. Overall, the document surveys the state of the art in content-based image mining and retrieval.
In this project, we proposed a Content Based Image Retrieval (CBIR) system which is used to retrieve a
relevant image from an outsized database. Textile images showed the way for the development of CBIR. It
establishes the efficient combination of color, shape and texture features. Here the textile image is given as
dataset. The images in database are loaded. The resultant image is given as input to feature extraction
technique which is transformation of input image into a set of features such as color, texture and shape.
The texture feature of an image is taken out by using Gray level co-occurrence matrix (GLCM). The color
feature of an image is obtained by HSI color space. The shape feature of an image is extorted by sobel
technique. These algorithms are used to calculate the similarity between extracted features. These features
are combined effectively so that the retrieval accuracy and recall rate is enhanced. The classification
techniques such as Support Vector Machine (SVM) are used to classify the features of a query image by
splitting the group such as color, shape and texture. Finally, the relevant images are retrieved from a large
database and hence the efficiency of an image is plotted.The software used is MATLAB 7.10 (matrix
laboratory) which is built software applications
C OMPARATIVE S TUDY OF D IMENSIONALITY R EDUCTION T ECHNIQUES U SING PCA AND ...csandit
The aim of this paper is to present a comparative s
tudy of two linear dimension reduction
methods namely PCA (Principal Component Analysis) a
nd LDA (Linear Discriminant Analysis).
The main idea of PCA is to transform the high dimen
sional input space onto the feature space
where the maximal variance is displayed. The featur
e selection in traditional LDA is obtained
by maximizing the difference between classes and mi
nimizing the distance within classes. PCA
finds the axes with maximum variance for the whole
data set where LDA tries to find the axes
for best class seperability. The proposed method is
experimented over a general image database
using Matlab. The performance of these systems has
been evaluated by Precision and Recall
measures. Experimental results show that PCA based
dimension reduction method gives the
better performance in terms of higher precision and
recall values with lesser computational
complexity than the LDA based method.
Web Image Retrieval Using Visual Dictionaryijwscjournal
In this research, we have proposed semantic based image retrieval system to retrieve set of relevant images for the given query image from the Web. We have used global color space model and Dense SIFT feature extraction technique to generate visual dictionary using proposed quantization algorithm. The images are transformed into set of features. These features are used as inputs in our proposed Quantization algorithm for generating the code word to form visual dictionary. These codewords are used to represent images semantically to form visual labels using Bag-of-Features (BoF). The Histogram intersection method is used to measure the distance between input image and the set of images in the image database to retrieve similar images. The experimental results are evaluated over a collection of 1000 generic Web images to demonstrate the effectiveness of the proposed system.
This document summarizes and reviews several techniques for image mining, including feature extraction, image clustering, and object recognition algorithms. It discusses color, texture, and edge feature extraction techniques and evaluates their precision and recall. It also describes the block truncation algorithm for image recognition and the cascade feature extraction approach. The key techniques - color moments, block truncation coding, and cascade classifiers - are evaluated based on experimental recall and precision results. Overall, the document provides an overview of different image mining techniques and evaluates their effectiveness.
Content Based Image and Video Retrieval AlgorithmAkshit Bum
The document describes content-based image and video retrieval (CBIR) algorithms. It discusses how CBIR works by extracting features from query images, indexing images, and retrieving similar images based on color, shape, and texture features. CBIR techniques include reverse image search, semantic retrieval using queries, and relevance feedback to refine searches based on user input about retrieved images. The document provides examples of CBIR applications in areas like crime prevention, military, web searching, and medical diagnosis.
A Study on Image Retrieval Features and Techniques with Various CombinationsIRJET Journal
This document discusses image retrieval techniques for content-based image retrieval systems. It begins with an introduction to the growth of digital image collections and the need for large-scale image retrieval systems. It then reviews different features used for image retrieval, such as color histograms, color moments, color coherence vectors, and discrete wavelet transforms. Edge features and corner features are also discussed. The document concludes that using only one feature type such as color or texture is not sufficient, and the best approach is to extract multiple high-quality features and combine them for image retrieval.
During past few years, people have been substantially attracted towards Content-Based Image Retrieval (CBIR) because of its varied multimedia applications. CBIR is one of the most popular research areas of digital image processing. The goal of the CBIR is to extract the visual features of the image such as color, texture or shape. An attempt is made to develop a Sketch Based Image Retrieval (SBIR) making it use of the features such as shape or form of the object [1]. This paper aims to introduce the creation and design of SBIR system making use of extraction techniques of Biased Maximum Margin Analysis (BMMA) and a Semi-Supervised Biased Maximum Margin Analysis (Semi BMMA) [2].. With the help of existing methods, design a task specific descriptor, which can handle the informational gap between a sketch and a colored image. The result of SBIR includes the set of positive images (relevant) and negative images (irrelevant). Iterative process acts on the positive set for the optimal extraction of the object. By using Laplacian regularizer to BMMA, the Semi BMMA integrates the information of unlabelled samples [2] to result in the better extraction of refined set of objects. The SBIR have several applications such as digital libraries, crime prevention, photo sharing sites etc. An important application is a matching a forensic image to gallery of mug shot images.
A SURVEY ON CONTENT BASED IMAGE RETRIEVAL USING MACHINE LEARNINGIRJET Journal
This document provides a literature review of recent research on content-based image retrieval using machine learning techniques. It summarizes 8 research papers that used approaches like convolutional neural networks, color histograms, deep learning, hashing functions and more to extract image features and retrieve similar images from databases. The goal of content-based image retrieval is to find images that are semantically similar to a query image based on visual features.
This document describes a proposed content-based image retrieval system using backpropagation neural networks (BPNN) and k-means clustering. It begins by discussing CBIR techniques and features like color, texture, and shape. It then outlines the proposed system which includes training a BPNN on image features, validating images, and testing by querying and retrieving similar images. Performance is analyzed based on metrics like accuracy, efficiency, and classification rate. Results show the system achieves up to 98% classification accuracy within 5-6 seconds.
The document reviews various feature extraction techniques that have been used for content-based image retrieval (CBIR) systems. It discusses several approaches for extracting color, texture, shape and spatial features from images. It also examines different similarity measures and evaluation methods for CBIR systems, including precision, recall and distance metrics. Feature extraction is a key factor for CBIR, and the paper provides an overview of some of the major techniques that have been explored for this task.
Precision face image retrieval by extracting the face features and comparing ...prjpublications
This document describes a proposed method for improving content-based face image retrieval. The method uses two orthogonal techniques: attribute-enhanced sparse coding and attribute-embedded inverted indexing. Attribute-enhanced sparse coding exploits global features to construct semantic codewords offline. Attribute-embedded inverted indexing considers local query image features in a binary signature to efficiently retrieve images. By combining these techniques, the method reduces errors and achieves better face image extraction from databases compared to existing content-based retrieval systems. It works by extracting features from the query image, matching them to database images, and returning ranked results.
Web Image Retrieval Using Visual Dictionaryijwscjournal
In this research, we have proposed semantic based image retrieval system to retrieve set of relevant images for the given query image from the Web. We have used global color space model and Dense SIFT feature extraction technique to generate visual dictionary using proposed quantization algorithm. The images are transformed into set of features. These features are used as inputs in our proposed Quantization algorithm for generating the code word to form visual dictionary. These codewords are used to represent images semantically to form visual labels using Bag-of-Features (BoF). The Histogram intersection method is used to measure the distance between input image and the set of images in the image database to retrieve similar images. The experimental results are evaluated over a collection of 1000 generic Web images to demonstrate the effectiveness of the proposed system.
SIGNIFICANCE OF DIMENSIONALITY REDUCTION IN IMAGE PROCESSING sipij
The aim of this paper is to present a comparative study of two linear dimension reduction methods namely
PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis). The main idea of PCA is to
transform the high dimensional input space onto the feature space where the maximal variance is
displayed. The feature selection in traditional LDA is obtained by maximizing the difference between
classes and minimizing the distance within classes. PCA finds the axes with maximum variance for the
whole data set where LDA tries to find the axes for best class seperability. The neural network is trained
about the reduced feature set (using PCA or LDA) of images in the database for fast searching of images
from the database using back propagation algorithm. The proposed method is experimented over a general
image database using Matlab. The performance of these systems has been evaluated by Precision and
Recall measures. Experimental results show that PCA gives the better performance in terms of higher
precision and recall values with lesser computational complexity than LDA
Improving Graph Based Model for Content Based Image RetrievalIRJET Journal
This document summarizes a research paper that proposes improvements to a graph-based model called Manifold Ranking (MR) for content-based image retrieval. Specifically, it introduces a novel scalable graph-based ranking model called Efficient Manifold Ranking (EMR) that addresses shortcomings of MR in scalable graph construction and efficient ranking computation. The proposed EMR model builds an anchor graph on the database instead of a traditional k-nearest neighbor graph, and designs a new form of adjacency matrix to speed up the ranking computation. Experimental results on large image databases demonstrate that EMR is effective for real-world image retrieval applications.
This document provides a comprehensive review of recent developments in content-based image retrieval and feature extraction. It discusses various low-level visual features used for image retrieval, including color, texture, shape, and spatial features. It also reviews approaches that fuse low-level features and use local features. Machine learning and deep learning techniques for content-based image retrieval are also summarized. The document concludes by discussing open challenges and directions for future research in this area.
This document summarizes an approach for content-based image retrieval using histograms. It discusses representing images as Histogram Attributed Relational Graphs (HARGs) where each node is an image region and edges represent relations between regions. A query is converted to a FARG which is compared to database FARGs using a graph matching algorithm. The system was tested on a database of natural images and performance was quantified using standard measures. It achieved good retrieval results but leaves room for improving retrieval time and reducing semantic gaps between low-level features and human perceptions.
IMAGE SEARCH USING SIMILARITY MEASURES BASED ON CIRCULAR SECTORScscpconf
With growing number of stored image data, image search and image similarity problem become
more and more important. The answer can be solved by Content-Based Image Retrieval
systems. This paper deals with an image search using similarity measures based on circular
sectors method. The method is inspired by human eye functionality. The main contribution of the
paper is a modified method that increases accuracy for about 8% in comparison with original
approach. Here proposed method has used HSB colour model and median function for feature
extraction. The original approach uses RGB colour model with mean function. Implemented
method was validated on 10 image categories where overall average precision was 67%.
Literature Review on Content Based Image RetrievalUpekha Vandebona
This document summarizes a literature review on content-based image retrieval (CBIR). It discusses how CBIR uses computer vision techniques to automatically extract visual features from images for retrieval, unlike traditional concept-based methods that rely on metadata/text. The key visual features discussed are color, texture, and shape. A typical CBIR system architecture includes creating an image database, automatically extracting features, searching by example or semantics, and ranking results. Distance measures are used to compare image features and evaluate retrieval performance. Combining CBIR with concept-based techniques could improve image retrieval overall.
Research Inventy : International Journal of Engineering and Scienceinventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
This document describes a sketch-based image retrieval system that uses freehand sketches as queries to retrieve similar colored images from a database. The system first extracts features like color, texture, and shape from the sketch using descriptors such as Color and Edge Directivity Descriptor (CEDD) and Edge Histogram Descriptor (EHD). It then clusters the images in the database using k-means clustering based on the similarity of their features to the sketch. Finally, the system retrieves the most similar colored image from the clustered images as the output match for the user's sketch query.
META-HEURISTICS BASED ARF OPTIMIZATION FOR IMAGE RETRIEVALIJCSEIT Journal
The document proposes an approach combining automatic relevance feedback and particle swarm optimization for image retrieval. It constructs a visual feature database from image features like color moments and Gabor filters. For a query image, it retrieves similar images and generates automatic relevance feedback by labeling images as relevant or irrelevant. It then uses particle swarm optimization to re-weight features and retrieve more relevant images over multiple iterations, splitting the swarm in later iterations. An experiment on Corel images over 5 classes showed the approach could effectively retrieve relevant images through this meta-heuristic process without human interaction.
Novel Hybrid Approach to Visual Concept Detection Using Image AnnotationCSCJournals
Millions of images are being uploaded on the internet without proper description (tags) about these images. Image retrieval based on image tagging approach is much faster than Content Based Image Retrieval (CBIR) approach but requires an entire image collection to be manually annotated with proper tags. This requires a lot of human efforts and time, and hence not feasible for huge image collections. An efficient method is necessary for automatically tagging such a vast collection of images. We propose a novel image tagging method, which automatically tags any image with its concept. Our unique approach to solve this problem involves manual tagging of small exemplar image set and low-level feature extraction of all the images, hence called a hybrid approach. This approach can be used to tag a large image dataset from manually tagged small image dataset. The experiments are performed on Wang's Corel Dataset. In the comparative study, it is found that, the proposed concept detection system based on this novel tagging approach has much less time complexity of classification step, and results in significant improvement in accuracy as compared to the other tagging approaches found in the literature. This approach may be used as faster alternative to the typical Content Based Image Retrieval (CBIR) approach for domain specific applications.
WEB IMAGE RETRIEVAL USING CLUSTERING APPROACHEScscpconf
Image retrieval system is an active area to propose a new approach to retrieve images from the
large image database. In this concerned, we proposed an algorithm to represent images using
divisive based and partitioned based clustering approaches. The HSV color component and Haar wavelet transform is used to extract image features. These features are taken to segment an image to obtain objects. For segmenting an image, we used modified k-means clustering algorithm to group similar pixel together into K groups with cluster centers. To modify Kmeans, we proposed a divisive based clustering algorithm to determine the number of cluster and get back with number of cluster to k-means to obtain significant object groups. In addition, we also discussed the similarity distance measure using threshold value and object uniqueness to quantify the results.
The content based Image Retrieval is the restoration of images with respect to the visual appearances
like texture, shape and color.The methods, components and the algorithms adopted in this content based
retrieval of images were commonly derived from the areas like pattern identification, signal progressing
and the computer vision. Moreover the shape and the color features were abstracted in the course of
wavelet transformation and color histogram. Thus the new content based retrieval is proposed in this
research paper.In this paper the algorithms were required to propose with regards to the shape, shade and
texture feature abstraction .The concept of discrete wavelet transform to be implemented in order to
compute the Euclidian distance.The calculation of clusters was made with the help of the modified KMeans
clustering technique. Thus the analysis is made in among the query image and the database
image.The MATLAB software is implemented to execute the queries. The K-Means of abstraction is
proposed by performing fragmentation and grid-means module, feature extraction and K- nearest neighbor
clustering algorithms to construct the content based image retrieval system.Thus the obtained result are
made to compute and compared to all other algorithm for the retrieval of quality image features
Cartoon Based Image Retrieval : An Indexing Approachmlaij
This paper proposes a methodology for the content based image retrieval which is implemented on the
cartoon images. The similarities between a query cartoon character image and the images in database are
computed by the feature extraction using the fusion descriptors of SIFT (Scale Invariant Feature
Transforms) and HOG (Histogram of Gradient). Based on the similarities, the cartoon images same or
similar to query images are identified and retrieved. This method makes use of indexing technique for more
efficient and scalable retrieval of the cartoon character. The experiment results demonstrate that the
proposed method is efficient in retrieving the cartoon images from the large database.
Similar to International Journal of Engineering Research and Development (20)
A Novel Method for Prevention of Bandwidth Distributed Denial of Service AttacksIJERD Editor
Distributed Denial of Service (DDoS) Attacks became a massive threat to the Internet. Traditional
Architecture of internet is vulnerable to the attacks like DDoS. Attacker primarily acquire his army of Zombies,
then that army will be instructed by the Attacker that when to start an attack and on whom the attack should be
done. In this paper, different techniques which are used to perform DDoS Attacks, Tools that were used to
perform Attacks and Countermeasures in order to detect the attackers and eliminate the Bandwidth Distributed
Denial of Service attacks (B-DDoS) are reviewed. DDoS Attacks were done by using various Flooding
techniques which are used in DDoS attack.
The main purpose of this paper is to design an architecture which can reduce the Bandwidth
Distributed Denial of service Attack and make the victim site or server available for the normal users by
eliminating the zombie machines. Our Primary focus of this paper is to dispute how normal machines are
turning into zombies (Bots), how attack is been initiated, DDoS attack procedure and how an organization can
save their server from being a DDoS victim. In order to present this we implemented a simulated environment
with Cisco switches, Routers, Firewall, some virtual machines and some Attack tools to display a real DDoS
attack. By using Time scheduling, Resource Limiting, System log, Access Control List and some Modular
policy Framework we stopped the attack and identified the Attacker (Bot) machines
Hearing loss is one of the most common human impairments. It is estimated that by year 2015 more
than 700 million people will suffer mild deafness. Most can be helped by hearing aid devices depending on the
severity of their hearing loss. This paper describes the implementation and characterization details of a dual
channel transmitter front end (TFE) for digital hearing aid (DHA) applications that use novel micro
electromechanical- systems (MEMS) audio transducers and ultra-low power-scalable analog-to-digital
converters (ADCs), which enable a very-low form factor, energy-efficient implementation for next-generation
DHA. The contribution of the design is the implementation of the dual channel MEMS microphones and powerscalable
ADC system.
Influence of tensile behaviour of slab on the structural Behaviour of shear c...IJERD Editor
-A composite beam is composed of a steel beam and a slab connected by means of shear connectors
like studs installed on the top flange of the steel beam to form a structure behaving monolithically. This study
analyzes the effects of the tensile behavior of the slab on the structural behavior of the shear connection like slip
stiffness and maximum shear force in composite beams subjected to hogging moment. The results show that the
shear studs located in the crack-concentration zones due to large hogging moments sustain significantly smaller
shear force and slip stiffness than the other zones. Moreover, the reduction of the slip stiffness in the shear
connection appears also to be closely related to the change in the tensile strain of rebar according to the increase
of the load. Further experimental and analytical studies shall be conducted considering variables such as the
reinforcement ratio and the arrangement of shear connectors to achieve efficient design of the shear connection
in composite beams subjected to hogging moment.
Gold prospecting using Remote Sensing ‘A case study of Sudan’IJERD Editor
Gold has been extracted from northeast Africa for more than 5000 years, and this may be the first
place where the metal was extracted. The Arabian-Nubian Shield (ANS) is an exposure of Precambrian
crystalline rocks on the flanks of the Red Sea. The crystalline rocks are mostly Neoproterozoic in age. ANS
includes the nations of Israel, Jordan. Egypt, Saudi Arabia, Sudan, Eritrea, Ethiopia, Yemen, and Somalia.
Arabian Nubian Shield Consists of juvenile continental crest that formed between 900 550 Ma, when intra
oceanic arc welded together along ophiolite decorated arc. Primary Au mineralization probably developed in
association with the growth of intra oceanic arc and evolution of back arc. Multiple episodes of deformation
have obscured the primary metallogenic setting, but at least some of the deposits preserve evidence that they
originate as sea floor massive sulphide deposits.
The Red Sea Hills Region is a vast span of rugged, harsh and inhospitable sector of the Earth with
inimical moon-like terrain, nevertheless since ancient times it is famed to be an abode of gold and was a major
source of wealth for the Pharaohs of ancient Egypt. The Pharaohs old workings have been periodically
rediscovered through time. Recent endeavours by the Geological Research Authority of Sudan led to the
discovery of a score of occurrences with gold and massive sulphide mineralizations. In the nineties of the
previous century the Geological Research Authority of Sudan (GRAS) in cooperation with BRGM utilized
satellite data of Landsat TM using spectral ratio technique to map possible mineralized zones in the Red Sea
Hills of Sudan. The outcome of the study mapped a gossan type gold mineralization. Band ratio technique was
applied to Arbaat area and a signature of alteration zone was detected. The alteration zones are commonly
associated with mineralization. The alteration zones are commonly associated with mineralization. A filed check
confirmed the existence of stock work of gold bearing quartz in the alteration zone. Another type of gold
mineralization that was discovered using remote sensing is the gold associated with metachert in the Atmur
Desert.
Reducing Corrosion Rate by Welding DesignIJERD Editor
This document summarizes a study on reducing corrosion rates in steel through welding design. The researchers tested different welding groove designs (X, V, 1/2X, 1/2V) and preheating temperatures (400°C, 500°C, 600°C) on ferritic malleable iron samples. Testing found that X and V groove designs with 500°C and 600°C preheating had corrosion rates of 0.5-0.69% weight loss after 14 days, compared to 0.57-0.76% for 400°C preheating. Higher preheating reduced residual stresses which decreased corrosion. Residual stresses were 1.7 MPa for optimal X groove and 600°C
Router 1X3 – RTL Design and VerificationIJERD Editor
Routing is the process of moving a packet of data from source to destination and enables messages
to pass from one computer to another and eventually reach the target machine. A router is a networking device
that forwards data packets between computer networks. It is connected to two or more data lines from different
networks (as opposed to a network switch, which connects data lines from one single network). This paper,
mainly emphasizes upon the study of router device, it‟s top level architecture, and how various sub-modules of
router i.e. Register, FIFO, FSM and Synchronizer are synthesized, and simulated and finally connected to its top
module.
Active Power Exchange in Distributed Power-Flow Controller (DPFC) At Third Ha...IJERD Editor
This paper presents a component within the flexible ac-transmission system (FACTS) family, called
distributed power-flow controller (DPFC). The DPFC is derived from the unified power-flow controller (UPFC)
with an eliminated common dc link. The DPFC has the same control capabilities as the UPFC, which comprise
the adjustment of the line impedance, the transmission angle, and the bus voltage. The active power exchange
between the shunt and series converters, which is through the common dc link in the UPFC, is now through the
transmission lines at the third-harmonic frequency. DPFC multiple small-size single-phase converters which
reduces the cost of equipment, no voltage isolation between phases, increases redundancy and there by
reliability increases. The principle and analysis of the DPFC are presented in this paper and the corresponding
simulation results that are carried out on a scaled prototype are also shown.
Mitigation of Voltage Sag/Swell with Fuzzy Control Reduced Rating DVRIJERD Editor
Power quality has been an issue that is becoming increasingly pivotal in industrial electricity
consumers point of view in recent times. Modern industries employ Sensitive power electronic equipments,
control devices and non-linear loads as part of automated processes to increase energy efficiency and
productivity. Voltage disturbances are the most common power quality problem due to this the use of a large
numbers of sophisticated and sensitive electronic equipment in industrial systems is increased. This paper
discusses the design and simulation of dynamic voltage restorer for improvement of power quality and
reduce the harmonics distortion of sensitive loads. Power quality problem is occurring at non-standard
voltage, current and frequency. Electronic devices are very sensitive loads. In power system voltage sag,
swell, flicker and harmonics are some of the problem to the sensitive load. The compensation capability
of a DVR depends primarily on the maximum voltage injection ability and the amount of stored
energy available within the restorer. This device is connected in series with the distribution feeder at
medium voltage. A fuzzy logic control is used to produce the gate pulses for control circuit of DVR and the
circuit is simulated by using MATLAB/SIMULINK software.
Study on the Fused Deposition Modelling In Additive ManufacturingIJERD Editor
Additive manufacturing process, also popularly known as 3-D printing, is a process where a product
is created in a succession of layers. It is based on a novel materials incremental manufacturing philosophy.
Unlike conventional manufacturing processes where material is removed from a given work price to derive the
final shape of a product, 3-D printing develops the product from scratch thus obviating the necessity to cut away
materials. This prevents wastage of raw materials. Commonly used raw materials for the process are ABS
plastic, PLA and nylon. Recently the use of gold, bronze and wood has also been implemented. The complexity
factor of this process is 0% as in any object of any shape and size can be manufactured.
Spyware triggering system by particular string valueIJERD Editor
This computer programme can be used for good and bad purpose in hacking or in any general
purpose. We can say it is next step for hacking techniques such as keylogger and spyware. Once in this system if
user or hacker store particular string as a input after that software continually compare typing activity of user
with that stored string and if it is match then launch spyware programme.
A Blind Steganalysis on JPEG Gray Level Image Based on Statistical Features a...IJERD Editor
This paper presents a blind steganalysis technique to effectively attack the JPEG steganographic
schemes i.e. Jsteg, F5, Outguess and DWT Based. The proposed method exploits the correlations between
block-DCTcoefficients from intra-block and inter-block relation and the statistical moments of characteristic
functions of the test image is selected as features. The features are extracted from the BDCT JPEG 2-array.
Support Vector Machine with cross-validation is implemented for the classification.The proposed scheme gives
improved outcome in attacking.
Secure Image Transmission for Cloud Storage System Using Hybrid SchemeIJERD Editor
- Data over the cloud is transferred or transmitted between servers and users. Privacy of that
data is very important as it belongs to personal information. If data get hacked by the hacker, can be
used to defame a person’s social data. Sometimes delay are held during data transmission. i.e. Mobile
communication, bandwidth is low. Hence compression algorithms are proposed for fast and efficient
transmission, encryption is used for security purposes and blurring is used by providing additional
layers of security. These algorithms are hybridized for having a robust and efficient security and
transmission over cloud storage system.
Application of Buckley-Leverett Equation in Modeling the Radius of Invasion i...IJERD Editor
A thorough review of existing literature indicates that the Buckley-Leverett equation only analyzes
waterflood practices directly without any adjustments on real reservoir scenarios. By doing so, quite a number
of errors are introduced into these analyses. Also, for most waterflood scenarios, a radial investigation is more
appropriate than a simplified linear system. This study investigates the adoption of the Buckley-Leverett
equation to estimate the radius invasion of the displacing fluid during waterflooding. The model is also adopted
for a Microbial flood and a comparative analysis is conducted for both waterflooding and microbial flooding.
Results shown from the analysis doesn’t only records a success in determining the radial distance of the leading
edge of water during the flooding process, but also gives a clearer understanding of the applicability of
microbes to enhance oil production through in-situ production of bio-products like bio surfactans, biogenic
gases, bio acids etc.
Gesture Gaming on the World Wide Web Using an Ordinary Web CameraIJERD Editor
- Gesture gaming is a method by which users having a laptop/pc/x-box play games using natural or
bodily gestures. This paper presents a way of playing free flash games on the internet using an ordinary webcam
with the help of open source technologies. Emphasis in human activity recognition is given on the pose
estimation and the consistency in the pose of the player. These are estimated with the help of an ordinary web
camera having different resolutions from VGA to 20mps. Our work involved giving a 10 second documentary to
the user on how to play a particular game using gestures and what are the various kinds of gestures that can be
performed in front of the system. The initial inputs of the RGB values for the gesture component is obtained by
instructing the user to place his component in a red box in about 10 seconds after the short documentary before
the game is finished. Later the system opens the concerned game on the internet on popular flash game sites like
miniclip, games arcade, GameStop etc and loads the game clicking at various places and brings the state to a
place where the user is to perform only gestures to start playing the game. At any point of time the user can call
off the game by hitting the esc key and the program will release all of the controls and return to the desktop. It
was noted that the results obtained using an ordinary webcam matched that of the Kinect and the users could
relive the gaming experience of the free flash games on the net. Therefore effective in game advertising could
also be achieved thus resulting in a disruptive growth to the advertising firms.
Hardware Analysis of Resonant Frequency Converter Using Isolated Circuits And...IJERD Editor
-LLC resonant frequency converter is basically a combo of series as well as parallel resonant ckt. For
LCC resonant converter it is associated with a disadvantage that, though it has two resonant frequencies, the
lower resonant frequency is in ZCS region[5]. For this application, we are not able to design the converter
working at this resonant frequency. LLC resonant converter existed for a very long time but because of
unknown characteristic of this converter it was used as a series resonant converter with basically a passive
(resistive) load. . Here, it was designed to operate in switching frequency higher than resonant frequency of the
series resonant tank of Lr and Cr converter acts very similar to Series Resonant Converter. The benefit of LLC
resonant converter is narrow switching frequency range with light load[6] . Basically, the control ckt plays a
very imp. role and hence 555 Timer used here provides a perfect square wave as the control ckt provides no
slew rate which makes the square wave really strong and impenetrable. The dead band circuit provides the
exclusive dead band in micro seconds so as to avoid the simultaneous firing of two pairs of IGBT’s where one
pair switches off and the other on for a slightest period of time. Hence, the isolator ckt here is associated with
each and every ckt used because it acts as a driver and an isolation to each of the IGBT is provided with one
exclusive transformer supply[3]. The IGBT’s are fired using the appropriate signal using the previous boards
and hence at last a high frequency rectifier ckt with a filtering capacitor is used to get an exact dc
waveform .The basic goal of this particular analysis is to observe the wave forms and characteristics of
converters with differently positioned passive elements in the form of tank circuits.
Simulated Analysis of Resonant Frequency Converter Using Different Tank Circu...IJERD Editor
LLC resonant frequency converter is basically a combo of series as well as parallel resonant ckt. For
LCC resonant converter it is associated with a disadvantage that, though it has two resonant frequencies, the
lower resonant frequency is in ZCS region [5]. For this application, we are not able to design the converter
working at this resonant frequency. LLC resonant converter existed for a very long time but because of
unknown characteristic of this converter it was used as a series resonant converter with basically a passive
(resistive) load. . Here, it was designed to operate in switching frequency higher than resonant frequency of the
series resonant tank of Lr and Cr converter acts very similar to Series Resonant Converter. The benefit of LLC
resonant converter is narrow switching frequency range with light load[6] . Basically, the control ckt plays a
very imp. role and hence 555 Timer used here provides a perfect square wave as the control ckt provides no
slew rate which makes the square wave really strong and impenetrable. The dead band circuit provides the
exclusive dead band in micro seconds so as to avoid the simultaneous firing of two pairs of IGBT’s where one
pair switches off and the other on for a slightest period of time. Hence, the isolator ckt here is associated with
each and every ckt used because it acts as a driver and an isolation to each of the IGBT is provided with one
exclusive transformer supply[3]. The IGBT’s are fired using the appropriate signal using the previous boards
and hence at last a high frequency rectifier ckt with a filtering capacitor is used to get an exact dc
waveform .The basic goal of this particular analysis is to observe the wave forms and characteristics of
converters with differently positioned passive elements in the form of tank circuits. The supported simulation
is done through PSIM 6.0 software tool
Amateurs Radio operator, also known as HAM communicates with other HAMs through Radio
waves. Wireless communication in which Moon is used as natural satellite is called Moon-bounce or EME
(Earth -Moon-Earth) technique. Long distance communication (DXing) using Very High Frequency (VHF)
operated amateur HAM radio was difficult. Even with the modest setup having good transceiver, power
amplifier and high gain antenna with high directivity, VHF DXing is possible. Generally 2X11 YAGI antenna
along with rotor to set horizontal and vertical angle is used. Moon tracking software gives exact location,
visibility of Moon at both the stations and other vital data to acquire real time position of moon.
“MS-Extractor: An Innovative Approach to Extract Microsatellites on „Y‟ Chrom...IJERD Editor
Simple Sequence Repeats (SSR), also known as Microsatellites, have been extensively used as
molecular markers due to their abundance and high degree of polymorphism. The nucleotide sequences of
polymorphic forms of the same gene should be 99.9% identical. So, Microsatellites extraction from the Gene is
crucial. However, Microsatellites repeat count is compared, if they differ largely, he has some disorder. The Y
chromosome likely contains 50 to 60 genes that provide instructions for making proteins. Because only males
have the Y chromosome, the genes on this chromosome tend to be involved in male sex determination and
development. Several Microsatellite Extractors exist and they fail to extract microsatellites on large data sets of
giga bytes and tera bytes in size. The proposed tool “MS-Extractor: An Innovative Approach to extract
Microsatellites on „Y‟ Chromosome” can extract both Perfect as well as Imperfect Microsatellites from large
data sets of human genome „Y‟. The proposed system uses string matching with sliding window approach to
locate Microsatellites and extracts them.
Importance of Measurements in Smart GridIJERD Editor
- The need to get reliable supply, independence from fossil fuels, and capability to provide clean
energy at a fixed and lower cost, the existing power grid structure is transforming into Smart Grid. The
development of a smart energy distribution grid is a current goal of many nations. A Smart Grid should have
new capabilities such as self-healing, high reliability, energy management, and real-time pricing. This new era
of smart future grid will lead to major changes in existing technologies at generation, transmission and
distribution levels. The incorporation of renewable energy resources and distribution generators in the existing
grid will increase the complexity, optimization problems and instability of the system. This will lead to a
paradigm shift in the instrumentation and control requirements for Smart Grids for high quality, stable and
reliable electricity supply of power. The monitoring of the grid system state and stability relies on the
availability of reliable measurement of data. In this paper the measurement areas that highlight new
measurement challenges, development of the Smart Meters and the critical parameters of electric energy to be
monitored for improving the reliability of power systems has been discussed.
Study of Macro level Properties of SCC using GGBS and Lime stone powderIJERD Editor
The document summarizes a study on the use of ground granulated blast furnace slag (GGBS) and limestone powder to replace cement in self-compacting concrete (SCC). Tests were conducted on SCC mixes with 0-50% replacement of cement with GGBS and 0-20% replacement with limestone powder. The results showed that replacing 30% of cement with GGBS and 15% with limestone powder produced SCC with the highest compressive strength of 46MPa, meeting fresh property requirements. The study concluded that this ternary blend of cement, GGBS and limestone powder can improve SCC properties while reducing costs.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Low power architecture of logic gates using adiabatic techniquesnooriasukmaningtyas
The growing significance of portable systems to limit power consumption in ultra-large-scale-integration chips of very high density, has recently led to rapid and inventive progresses in low-power design. The most effective technique is adiabatic logic circuit design in energy-efficient hardware. This paper presents two adiabatic approaches for the design of low power circuits, modified positive feedback adiabatic logic (modified PFAL) and the other is direct current diode based positive feedback adiabatic logic (DC-DB PFAL). Logic gates are the preliminary components in any digital circuit design. By improving the performance of basic gates, one can improvise the whole system performance. In this paper proposed circuit design of the low power architecture of OR/NOR, AND/NAND, and XOR/XNOR gates are presented using the said approaches and their results are analyzed for powerdissipation, delay, power-delay-product and rise time and compared with the other adiabatic techniques along with the conventional complementary metal oxide semiconductor (CMOS) designs reported in the literature. It has been found that the designs with DC-DB PFAL technique outperform with the percentage improvement of 65% for NOR gate and 7% for NAND gate and 34% for XNOR gate over the modified PFAL techniques at 10 MHz respectively.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor
Slides from talk presenting:
Aleš Zamuda: Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapter and Networking.
Presentation at IcETRAN 2024 session:
"Inter-Society Networking Panel GRSS/MTT-S/CIS
Panel Session: Promoting Connection and Cooperation"
IEEE Slovenia GRSS
IEEE Serbia and Montenegro MTT-S
IEEE Slovenia CIS
11TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONIC AND COMPUTING ENGINEERING
3-6 June 2024, Niš, Serbia
Generative AI leverages algorithms to create various forms of content
International Journal of Engineering Research and Development
1. International Journal of Engineering Research and Development
e-ISSN: 2278-067X, p-ISSN: 2278-800X, www.ijerd.com
Volume 10, Issue 4 (April 2014), PP.19-29
19
Modified Optimization by Artificial Bee Colony (MOABC) for
improvement in content-based image retrieval performance
Anil Kumar Mishra1
, Madhabananda Das2
, Tarini Charan Panda3
1
Orissa Engineering College, Bhubaneswar
2
KIIT University, Bhubaneswar
3
Orissa Engineering College Bhubaneswar
Abstract:- Artificial Bee Colony(ABC) algorithm based optimization techniques are quite popular and focus for
research in fields of Swarm Intelligence. This paper presents an optimization algorithm based on artificial bee
colony (ABC) to deal with optimization problems in Content Based Image Retrieval (CBIR) System. We have
introduced a modified ABC algorithm which is based on the intelligent scavenging behaviour for content base
images. It uses less control parameters, and it can be efficiently used for solving for multi object optimization
problems. In the current work, MOABC for discrete variables has been developed and implemented successfully
for the multi variable design optimization of composites. The proposed algorithm is applied to Image retrieval
section of CBIR system for minimizing the feature vectors and to find the image similar to the queried image.
Finally the performance is evaluated in comparison with other nature inspired techniques which includes
Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The performance of MOABC is better as par
with that of ABC, PSO and GA for all the loading configurations.
Keywords:- Multi variable optimization, Structural optimization, Artificial Bee Colony (ABC), Feature
Extraction.
I. INTRODUCTION
Content-based image retrieval is a method which expenditures visual contents to search images from
bulky scale image databases according to users interests it is also known as query by image content. Since 1900s
Content-based image retrieval has been an active and fast advancing research area. [1][5] CBIR comes to picture
when many applications with large image database, traditional methods of image indexing have recognized to
be unsatisfactory. Finger print scanning system, Automatic face recognition system, Medical image database,
Trademark image registration are the application of Content-based image retrieval (CBIR). [6][7] The process of
CBIR consists of three stages namely Image acquisition, Feature Extraction and Similarity Matching. In CBIR
first of all query image undergoes the three stages as mentioned above. The query image is then compared with
the images in the image database. All the images in the database undergo feature extraction so that the resultant
feature vector can be compared with the feature vector of the query image. The closest image in comparison
with the query image from the feature database is returned [8][10].
Multivariable optimization is defined as a problem of finding a vector of decision variables which
fulfils constraints and enhances a vector function whose element characterizes the objective functions and these
functions form a mathematical description of performance criteria which are usually in clash with each other [9]
[11]. The biggest problem for CBIR system is to incorporate useful techniques so as to procedure images of
expanded features and types. There are numerous methods for processing of low level cues are distinguished by
the characteristics of domain-images.[14] The performance of these methods is tested by various issues like
image resolution, intra-image illumination variations, non-homogeneity of intra-region and inter-region textures,
multiple and occluded objects etc. [4] [15] The other key trouble, is a gap between inferred understanding
semantics by pixel domain processing using low level cues and human perceptions of visual cues of given
image[16]. The content based image retrieval system comprises of multiple inter-dependent tasks performed by
various phases and Inter-tuning of all these phases of the content based retrieval system is unavoidable for over
all good results. The diversity in the images and semantic-gap generally enforce parameter tuning & threshold-
value specification suiting to the requirements [17].
In 2005 Karaboga proposed the artificial bee colony algorithm (ABC) is an optimization algorithm
based on the intelligent foraging behaviour of honey bee swarm [18]. In ABC model, the colony consists of
three groups of bees namely employed bees, onlookers and scouts. In this model it is assumed that there is only
one artificial employed bee for each food source [19]. Employed bees go to their food source and come back to
hive and dance on this area. The employed bee whose food source has been abandoned becomes a scout and
starts to search for finding a new food source [2]. Onlookers watch the dances of employed bees and choose
2. Modified Optimization by Artificial Bee Colony (MOABC) for improvement in content-based image retrieval...
20
food sources depending on dances. Optimal multi-level thresholding, MR brain image classification, cluster
analysis, face pose estimation and 2D protein folding etc. these are the applications of ABC [20].
II. RELATED RESEARCHES: A REVIEW
Despite a plenty of works available in the literature, a handful of significant research works are reviewed here.
Chih-Chin Laiet.al [21] have discussed about a user-oriented mechanism for CBIR method and in this
paper we used interactive genetic algorithm. IGA is a branch of evolutionary computation. The main difference
between IGA and GA is the construction of the fitness function, i.e., the fitness is determined by the user‟s
evaluation and not by the predefined mathematical formula. A user can interactively determine which members
of the population will reproduce, and IGA automatically generates the next generation of content based on the
user‟s input. Through repeated rounds of content generation and fitness assignment, IGA enables unique content
to evolve that suits the user‟s preferences. Based on this reason, IGA can be used to solve problems that are
difficult or impossible to formulate a computational fitness function, for example, evolving images, music,
various artistic designs, and forms to fit a user‟s aesthetic preferences. In this paper IGA is employed to help the
users identify the images that are most satisfied to the users‟ need.
Lei Wu et.al [22] have proposed a method on Tag Completion for Image Retrieval, the proposed
method falls into the category of semi-supervised learning in that both tagged images and untagged images were
exploited to find the optimal tag matrix. In this proposed method also evaluated tag completion by performing
two sets of experiments, i.e., automatic image annotation and tag based image retrieval. In this proposed method,
they focus on a study the problem of tag completion where the goal was to automatically fill in the missing tags
as well as correct noisy tags for given images.
Yingying Wang et.al [23] have proposed a model image classification as a Multiple Instance Learning
(MIL) problem, by regarding each image as a bag composed of different regions/patches. In this proposed
method rate distortion multiple instance learning (RDMIL) to take advantage of witnesses to improve the
performance of MIL for image classification task. The objective function of RDMIL was decomposed into two
convex sub-problems, which can be solved by alternating technique. Especially they design a sequential method
to effectively optimize the RD sub problem. Experimental results on two real-world datasets demonstrate the
proposed RDMIL algorithm was effective and promising. That approached well illustrates the generative
process of witnesses and measures the diverse importance of instances in a probabilistic approach.
H. B Kekre et.al [24] have proposed a method on the selection of suitable similarity measure for
content based image retrieval. It contains the analysis done after the application of similarity measure named
Minkowski Distance from order first to fifth. It was also explains the effective use of similarity measure named
correlation distance in the form of angle „cosθ‟ between two vectors. In this proposed method the „Bins
Approach‟ explained the terms of computational complexity for feature extraction. It was based on histogram
partitioning of three colour planes. As histogram was partitioned into 3 parts, they could form 27 bins out of it.
These bins were directed to extract the features of images in the form of four statistical moments namely Mean,
Standard Deviation, Skewness and Kurtosis.
Ramadass Sudhir et.al [25] proposed an algorithm image retrieval technique, which used YUV colour
space and wavelet transform approach for feature extraction. Firstly, the colour space was quantified in non-
equal intervals, then constructed one dimension feature vector and represented the colour feature. Similarly, the
texture feature extraction was obtained by using wavelet. Finally, colour feature and texture feature were
combined based on wavelet transform. Based on the colour feature, the rich colour images, such as the type of
landscape images or planes, the colour characteristics of the use of regular search colours can be similar to the
image. The use of colour and texture features of wavelet transform was more suitable for segmentation of
objects and classification of related image from thousands of images. In which the retrieved images were much
similar when the YUV is used and the retrieval time is also less when comparing with the previous RGB and
HSV methods.
B.Ramamurthy et.al [26] have proposed to retrieve the medical images from huge volume of medical
databases. This requires the pre-processing, feature extraction, classification, retrieval and indexing steps in
order to develop an efficient medical image retrieval system. In this proposed work, for pre-processing step, the
image segmentation method have been carried out, for feature extraction, basic shape feature has been extracted
using canny edge detection algorithm, and for classification, K-means classification algorithm has been used.
They have realize that Canny Edge Detection and K-means clustering algorithms are quite useful for retrieval of
relevant images from image database. The results of this proposed indicate that the proposed approach offers
significant performance improvements in retrieval of medical images. Further, by fine tuning of shape feature
extraction and using other shape feature extraction methods, performance of the retrieval process can be
improved more.
3. Modified Optimization by Artificial Bee Colony (MOABC) for improvement in content-based image retrieval...
21
This paper is organised as follows: basics of multi-objective problems in CBIR are presented in Section
3. Details of the problem and its formulation are explained in Section 4. The numerical results and discussions
are presented in Section 5. Finally, the comparison of nature inspired techniques and conclusions are given in
Sections 6.
III. PROPOSED METHODOLOGY USING ARTIFICIAL BEE COLONY ALGORITHM
FOR CONTENT BASED IMAGE RETRIEVAL:
In this paper, we have introduced an optimization technique by using the ABC. The proposed ABC
algorithm is performing as neighbourhood search model that fine tunes the neighbourhood search property from
employed and onlooker bees that helps to converge faster than conventional PSO and GA. This optimization
algorithm is giving the benchmark model.
3.1. Content Based Image Retrieval with Modified optimization by ABC:
The approach supported retrieving images just like one chosen by the user is
named Content based Image Retrieval (CBIR). Each image is represented by Multi variable optimize
dimensional feature extra action. During this approach, Image process algorithms square
measure accustomed extract feature extraction that represent image properties like color, texture,
and shape that square measure the visual features. To retrieve the query image from the database images, a
similarity measures are notice to check the likeness between a question image and database images. One
among the most sanctifications of the CBIR approach is the possibility of an automatic retrieval process, instead
of the traditional keyword-based approach, which usually requires very laborious and time-consuming previous
annotation of database images. The CBIR technology has been employed in many applications like fingerprint
identification, diverseness data systems, digital libraries, crime interference, medicine, historical analysis etc.
3.2. Feature Extraction:
In this paper, we have presented a Modified Article Bee Colony (MOABC) algorithm to solving the
optimization problem in CBIR System. In our algorithm, we use all three type feature extraction, color, texture
and shape.
3.2.1. Color Feature Extraction:
A content-based image retrieval system is presented that computes color similarity among images i.e. it
supports querying with respect to color. Color is one of the most important features of objects in image. The
color histogram of each image is then stored in the database. When the user does the search by specifying the
query image, the system registers the proportion of each color of the query image and goes through all images in
the database to find those whose color histograms match those of the query most closely. The color histogram is
widely used as an important color feature indicating the content of the image, due to its robustness to scaling,
orientation, perspective, and occlusion of images. Initially, a smoothening operation is performed over each
frame of the shot segmented clips. Anisotropic diffusion is utilized in our proposed CBIR approach for the
smoothening of the frames, prior to color histogram. For, a sequence of frames, the anisotropic diffusion is
given by
ftbacdiv
t
f
),,( (2)
Where, fdiv is the divergence operator,
t
f
is the diffusion co-efficient and denotes the
gradient. cba ,, is followed by a normalization function which converts the three dimensional vector into a
single dimensional vector. Thus, the color histogram, another important feature for the proposed CBIR scheme
is extracted
3.2.2. Texture Feature Extraction:
Texture is a feature that is quite difficult to describe, and subjected to the difference of human
perception, and it is hard to extracted by segmentation, because segmentation unable to extract the whole texture
but the texture element. Given a texture vector which is indicated as nxxxx ,,, 21 , where n is the
dimension of the feature vector. We model the distribution of all samples by the following formula
M
i
ii xpxp
1
)()|( (3)
4. Modified Optimization by Artificial Bee Colony (MOABC) for improvement in content-based image retrieval...
22
Where )(xpi is a normal PDF which is a component of the GMM It is parameterized by a mean
vector i , and a covariance matrix iR :
ii
T
i
i
i xRx
R
xp
1
2/1 2
1
exp
||2
1
)( (4)
i is the weight of the component )(xpi , 10 i for all components, and 1 i . Mixture
model specified in equation (3) is called the Gaussian Mixture Model (GMM).
3.3.3. Shape Feature Extraction:
Shape is also an important low-level feature in image retrieval system; since an object, in most case,
can form by a set of shape (e.g. a car is consisted of a few rectangles and a few circles), most similar objects
have a high correlation in the set of shapes. Shape-based image retrieval should extract the shapes from images
by segmentation, and classify the shape, where each shape should have their own representation and should
variant to scaling, rotation, and transition. In shape-based image retrieval the user need to choose an reference
image or sketch a desired shape, since the user may not only want the shape that exact matched, so shape based
image retrieval should be able to identify similar shapes.
IV. Modified Optimization by Artificial Bee Colony (MOABC) Algorithm for
CBIR Systems:
In the ABC algorithm, the colony of artificial bees is classified into three categories: employed bees,
onlookers, and scouts. Employed bees are associated with a particular food source that they are currently
exploiting or are “employed” at. They carry with them information about this particular source and share the
information to onlookers. Onlooker bees are those bees that are waiting on the dance area in the hive for the
information to be shared by the employed bees about their food sources and then make decision to choose a food
source. A bee carrying out random search is called a scout. In the ABC algorithm, the first half of the colony
consists of the employed artificial bees, and the second half includes the onlookers. For every food source, there
is only one employed bee. Onlookers are placed on the food sources by using a probability-based selection
process. As the nectar amount of a food source increases, the probability value with which the food source is
preferred by onlookers increases.
Figure 1: Proposed Diagram of Multi variable optimization technique with ABC
In our proposed algorithm, we have used an optimization concept with ABC and archive strategy to
make the algorithm converge to the PSO and GA. The best advantage of MOABC is that it could use less
control parameters to get the most competitive performance. In order to demonstrate the performance of the
MOABC algorithm, we have compared the performance of the MOABC with those of GA, PSO optimization
algorithms on several Variables.
In the initialization phase, the ABC algorithm generates randomly distributed initial food source
positions )(xF of solutions, )(xF where denotes the size of employed bees or onlooker bees. Each
5. Modified Optimization by Artificial Bee Colony (MOABC) for improvement in content-based image retrieval...
23
solution ))(........,),(),(()( 21 xfxfxfxF n is a n -dimensional vector. Here, n is the number of
optimization parameters. And then evaluate each nectar amount. In the ABC algorithm, nectar amount is the
value of benchmark function.
Employed Bees’ Phase:
In the employed bees‟ phase, each employed bee finds a new food source iV in the neighbourhood of
its current source ix . The new food source is calculated using the following expression:
)( kjijijijij xxxV (8)
where )....3,2,1( mk and )....3,2,1( nj are randomly chosen indexes and ijik . k is a
random number between [-1, 1]. It controls the production of a neighbour food source position around ijx . Then
employed bee compares the new one against the current solution and memorizes the better one by means of a
greedy selection mechanism.
Onlooker Bees’ Phase:
In the onlooker bees‟ phase, each onlooker chooses a food source with a probability, which is related to
the nectar amount (fitness) of a food source shared by employed bees. Probability is calculated using the
following expression:
(9)
Here if is fitness function and ifit is the fitness after a transformation.
Scout Bee Phase:
In the scout bee phase, if a food source cannot be improved through a predetermined cycles, called
“limit”, it is removed from the population, and the employed bee of that food source becomes scout. The scout
bee finds a new random food source position using
M
i i
i
i
f it
f it
p
1 (10)
Here M is the number of food source and if is fitness function of the
th
i food source. Finally,
chose a candidate solution based on the section probability by “roulette wheel section”, method. The best ones
then get quite the same selection probability as the others and the algorithm stops progressing.
Pseudo code of MOABC algorithm:
1. Step 1: Generates randomly distributed initial food source in cycle = 1
2. Step 2: Initialize the food source positions (solutions)
3. Step 3: Evaluate the nectar amount (fitness function ) of food sources
4. repeat
5. Step 4: Employed Bees‟ Phase
6. For each employed bee
7. Produce new food source positions
8. Calculate the value
9. If new position better than previous position
10. Then memorizes the new position and forgets the old one.
11. End For.
12. Step 5: Calculate the values for the solution.
13. Step 6: Go to 2nd
Phase (Onlooker Bees)
14. For each onlooker bee
15. Chooses a food source depending on
16. Produce new food source positions
17. Calculate the value
18. If new position better than previous position
6. Modified Optimization by Artificial Bee Colony (MOABC) for improvement in content-based image retrieval...
24
19. Then memorizes the new position and forgets the old one.
20. End For
21. Step 7: Go to 2nd
Phase (Scout Bee Phase)
22. If there is an employed bee becomes scout
23. Then replace it with a new random source positions
24. Step 8: Memorize the best solution achieved so far
25. Step 9: Cycle = cycle + 1.
26. Step 10: until cycle = Maximum Cycle Number
27. Evaluate each particle in the size of home
28. Step 11: Perform the optimal solution check for all the particles:
29. if the Calculated value is best xi is dominated by the new
solution, then xi is replaced by the new solution.
Table 1: Pseudo code of MOABC algorithm
After Step 10.The proposed algorithm performance of the approach is based on the precision; recall
and F-measure cross over points. After evaluating Euclidean distance of the query image, the precision and
recall values are generated using the following equations (8), (9) and (10) in[27].
retrivdimagesofnoTotal
retreivedimagesrelevantofNo
precision
.
.
(8)
databaseinimagesrelevantofnoTotal
retreivedimagesrelevantofNo
recall
.
.
(9)
recallprecision
recallprecision
F
*
2 (10)
V. EXPERIMENTAL RESULT
We will introduce performance measures. For every algorithm, we will give the parameter settings. The
propose technique is tested on the image database of 500 variable images includes five categories as some
different types of butterflies and flowers with 200 images for each datasets. The precision, recall and F-measure
are calculated for the sample query images for each category using Eq. (8), (9) and (10). These measures are the
important parameters to judge the performance of the algorithms. Precision is the fraction of the relevant images
which has been retrieved, checks the completeness of the algorithm. Recall is the fraction of the relevant images
which has been retrieved, checks the accuracy of the algorithm. After that we compare with the proposed result
with single object optimization technique of ABC, PSO and GA.
Table 2: Total number of Retrieved dataset 1 images for MOABC, ABC, PSO and GA
MOABC ABC PSO GA
Precision 0.74 0.68 0.62 0.53
Recall 0.70 0.62 0.58 0.47
F-score 0.66 0.59 0.48 0.45
Table 3: Total number of Retrieved dataset 2 images for MOABC, ABC, PSO and GA
MOABC ABC PSO GA
Precision 0.80 0.78 0.29 0.24
Recall 0.64 0.67 0.48 0.15
F-score 0.57 0.51 0.38 0.17
Table 4: Total number of Retrieved dataset 3 images for MOABC, ABC, PSO and GA
MOABC ABC PSO GA
Precision 0.69 0.70 0.56 0.45
Recall 0.70 0.38 0.51 0.57
F-score 0.63 0.21 0.44 0.50
7. Modified Optimization by Artificial Bee Colony (MOABC) for improvement in content-based image retrieval...
25
Table 5: Total number of Retrieved dataset4 images for MOABC, ABC, PSO and GA
MOABC ABC PSO GA
Precision 0.55 0.51 0.47 0.51
Recall 0.60 0.44 0.43 0.24
F-score 0.49 0.29 0.39 0.28
Table 6: Total number of Retrieved dataset 5 images for MOABC, ABC, PSO and GA
MOABC ABC PSO GA
Precision 0.71 0.69 0.45 0.29
Recall 0.47 0.57 0.57 0.48
F-score 0.88 0.62 0.50 0.38
(a)
(b)
Figure. 2: a) query image, b) Result of the Color Feature
(a)
(b)
Figure 3 a) query image, b) Result of the Shape Feature
8. Modified Optimization by Artificial Bee Colony (MOABC) for improvement in content-based image retrieval...
26
(a)
(b)
Figure 4: a) query image, b) Result of the Texture Feature
0
10
20
30
40
50
60
70
80
MOABC ABC PSO GA
Retrieved image dataset fromdata set 1
Precision
Recall
F-score
Figure 5: Precision, recall and F-measure values for MOABC, ABC, PSO and GA of dataset 1
0
20
40
60
80
100
MOABC ABC PSO GA
Retrieved image dataset from data set 2
Precision
Recall
F-score
Figure 6: Precision, recall and F-measure values for MOABC, ABC, PSO and GA of dataset 2
9. Modified Optimization by Artificial Bee Colony (MOABC) for improvement in content-based image retrieval...
27
Figure7: Precision, recall and F-measure values for MOABC, ABC, PSO and GA of dataset 3
Figure 8: Precision, recall and F-measure values for MOABC, ABC, PSO and GA of dataset 4
Figure 9: Precision, recall and F-measure values for MOABC, ABC, PSO and GA of dataset 5
Compared to other optimization technique, our proposed work has produced better results. Our proposed work
has performed satisfactorily when its color, texture and shape feature was tested on the five different databases.
From the fig 5 to fig. 9 is observed that the propose MOABC has the precision rate as 78% and recall rate 70%,
but the other three algorithms have lesser and also F-measure of the MOABC is also better than the individual
ABC, PSO and GA algorithms. After the inclusion of the feature extraction, based on the given query image, the
retrieved color, texture and shape feature from images.
10. Modified Optimization by Artificial Bee Colony (MOABC) for improvement in content-based image retrieval...
28
In the all five different dataset retrieval were performed by querying five different images and for every
dataset we get precision, recall and F-score values is determined. The precision and recall value comparison
between the proposed extensive algorithms features extraction based on multi-variable optimization systems
shows that the performance of the proposed system is best over that of the existing systems. Thus the
comparisons are made using multiple features such as color, texture, shape that the proposed MOABC algorithm
performs better than the individual ABC, PSO and GA.
VI. CONCLUSION
In this paper the new method (MOABC) is introducing multi variable updation in ABC algorithm, PSO
and GA instead of single optimization Search. Here it is implemented in MATLAB and is tested by using
different evaluation functions. The parameter position is optimized for multiple object and the values of the
propposed method is compared with single object ABC, PSO and GA algorithm. In the proposed method the
fitness values are seems to be better for the Precision Recall and F-score value found to be better in all single
evaluationary algorithems. From the above results it is clear that the MOABC is the better optimization
technique while comparing to ABC, PSO and GA.
REFERENCES
[1]. Binitha S, S Siva Sathya, “A Survey of Bio inspired Optimization Algorithms”, International Journal of
Soft Computing and Engineering, Vol. 2, No. 2, pp. 137-151, 2012.
[2]. Nabeel Mohammed and David McG. Squire,"Effectiveness of ICF features for collection-specific
CBIR", 9th International Workshop on information Systems and Applications, Vol. 7836, pp. 83-95,
2013.
[3]. Jun Wang, Sanjiv Kumar, and Shih-Fu Chang,"Semi-Supervised Hashing for Large Scale Search",
IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 34, No. 12, pp. 2393- 2406,
2012.
[4]. Swati V. Sakhare and Vrushali G. Nasre,"Design of Feature Extraction in Content Based Image
Retrieval(CBIR) using Color and Texture", International Journal of Computer Science & Informatics,
Vol. 1, pp. 57-62, 2011.
[5]. Vibha Bhandari and Sandeep B.Patil,"Comparison of CBIR Techniques using DCT and FFT for
Feature Vector Generation", International Journal of Emerging Trends & Technology in Computer
Science, Vol. 1, No. 4, pp. 1-10, 2012.
[6]. Marıa Jimena Costa, Alexey Tsymbal, Matthias Hammon, Alexander Cavallaro, Michael
Suhling,Sascha Seifert and Dorin Comaniciu,"A Discriminative Distance Learning–Based CBIR
Framework for Characterization of Indeterminate Liver Lesions", Vol. 7075, pp. 92-104, 2012.
[7]. Wasim Khan, Shiv Kumar, Neetesh Gupta and Nilofar Khan,"Signature Based Approach For Image
Retrieval Using Color Histogram And Wavelet Transform", International Journal of Soft Computing
and Engineering, Vol. 1, No. 1, pp. 43-50 March 2011.
[8]. H.B.Kekre, Dhirendra Mishra, Stuti Narula and Vidhi Shah,"Color Feature Extraction for CBIR",
International Journal of Engineering Science and Technology, Vol.1, pp. 1- 10, 2011.
[9]. Robert Huitl, Georg Schroth, Sebastian Hilsenbeck, Florian Schweiger and Eckehard
Steinbach,"VirtualReference View Generation for CBIR-based Visual Pose Estimation", pp. 993-996,
2012.
[10]. Sri Rama Krishna, A. Guruva Reddy, M.N.Giri Prasad, K.Chandrabushan Rao, M. Madhavi, "Genetic
Algorithm Processor for Image Noise Filtering Using Evolvable Hardware," International Journal of
Image Processing, Vol. 4, No. 3, Pp.240-251, 2010.
[11]. Jakia Afruz, Va Juanna Wilson,"Frequency Domain Pseudo-color to Enhance Ultrasound Images," In.
Proc. of Computer and Information Science, Vol. 3, No. 4, Pp. 24-34, Nov. 2010.
[12]. Grant J. Scott, Matthew N. Klaric, Curt H. Davis and Chi-Ren Shyu,"Entropy-Balanced Bitmap Tree
for Shape-Based Object Retrieval From Large-Scale Satellite Imagery Databases", IEEE Transactions
On Geoscience and Remote Sensing, VOL. 49, pp. 5, 2011.
[13]. H. B. Kekre and Kavita Sonawane,"Effect of Similarity Measures for CBIR Using Bins Approach",
International Journal of Image Processing, Vol. 6, pp. 182-190, 2012.
[14]. Edward Kim, Sameer Antani, Xiaolei Huang, L.Rodney Long and Dina Demner-Fushman,"Using
Relevant Regions in Image Search and Query Refinement for Medical CBIR", Society for Imaging
Informatics in Medicine, Vol. 21, pp. 280-289. 2207.
[15]. Gerald Schaefer, "Content-Based Image Retrieval – Some Basics", Advances in Intelligent and Soft
Computing, Vol. 103, pp 21-29, 2011.
11. Modified Optimization by Artificial Bee Colony (MOABC) for improvement in content-based image retrieval...
29
[16]. Ch. Kavitha, B. Prabhakara Rao and A. Govardhan,"Image Retrieval Based On Color and Texture
Features of the Image Sub-blocks", International Journal of Computer Applications, Vol. 15, No.7, pp.
975 – 8887, 2011.
[17]. Sushil Kumar Singh, Aruna Kathane,"Various Methods for Edge Detection in Digital Image
Processing," International journal of computer science and technology, Vol. 2, No. 2, Pp. 188-190,
June 2011.
[18]. Gulfishan Firdose Ahmed, Raju Barskar,"A Study on Different Image Retrieval Techniques in Image
Processing, "International Journal of Soft Computing and Engineering, Vol. 1, No. 4, Pp.247-251, Sep.
2011.
[19]. V. Selvi, R. Umarani, “Comparative Study of Swarm Intelligence Techniques”, International Journal of
Research in Engineering Design, Vol 01, No. 01, pp. 37-41, April - July 2012.
[20]. Milos Subotic, Milan Tuba and NadezdaStanarevic, “Different approaches in parallelization of the
artificial bee colony algorithm”, International Journal of Mathematical Models And Methods in
Applied Sciences, Vol. 5, No. 4, pp. 755-762, 2011
[21]. Chih-Chin Lai, and Ying-Chuan Chen,"A User-Oriented Image Retrieval System Based on Interactive
Genetic Algorithm", IEEE Transactions on Instrumentation and Measurement, Vol. 60, No. 10, pp. 1-
10, 2011.
[22]. Lei Wu, Rong Jin and Anil K. Jain,"Tag Completion for Image Retrieval", IEEE Transactions On
Pattern Analysis and Machine Intelligence, Vol. 35, No. 3, pp. 716-727, 2013.
[23]. Yingying Wang, Chun Zhang and Zhihua Wang,"Rate Distortion Multiple Instance Learning For
Image Classification", IEEE International Confrence On Image Prossecing, PP. 3235-3240, 2013.
[24]. H. B. Kekre and Kavita Sonawane,"Bin Pixel Count, Mean and Total of Intensities Extracted From
Partitioned Equalized Histogram for CBIR", International Journal of Engineering Science and
Technology, Vol. 4, pp. 1233- 1240, 2012.
[25]. Ramadass Sudhir and S. Santhosh Baboo,"An Efficient CBIR Technique with YUV Color Space and
Texture Features", Computer Engineering and Intelligent Systems, Vol. 2, No.6, pp. 78-85, 2011.
[26]. B.Ramamurthy and K.R.Chandran,"CBIR: Shape-Based Image Retrieval Using Canny Edge Detection
and K-Means Clustering Algorithms for Medical Images", International Journal of Engineering Science
and Technology, Vol. 3, No.1, pp. 1870-1880, 2011.
[27]. H.B.Kekreand Dhirendra Mishra, "Color Feature Extraction for CBIR", International Journal of
Engineering Science and Technology (IJEST), Vol. 3 No.12 December 2011