Content Based Image Retrieval (CBIR) aims at retrieving the images from the database based on the user query which is visual form rather than the traditional text form. The applications of CBIR extend from surveillance to remote sensing, medical imaging to weather forecasting, and security systems to historical research and so on. Though extensive research is made on content based image retrieval in the spatial domain, we have most images in the internet which is JPEG compressed which pushes the need for image retrieval in the compressed domain itself rather than decoding it to raw format before comparison and retrieval. This research addresses the need to retrieve the images from the database based on the features extracted from the compressed domain along with the application of genetic algorithm in improving the retrieval results. The research focuses on various features and their levels of impact on improving the precision and recall parameters of the CBIR system. Our experimentation results also indicate that the CBIR features in compressed domain along with the genetic algorithm usage improves the results considerably when compared with the literature techniques.
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
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.
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-Survey of Image Object Recognition TechniquesIRJET Journal
This document discusses various techniques for object recognition in digital images. It begins by defining object recognition and describing its goals. It then outlines several important techniques for object recognition, including spatial relations, temporal relations, data retrieval in conventional databases, image extraction through mining, and content-based retrieval. For each technique, it provides examples and discusses how the technique can be used to recognize objects in images. The document concludes that object recognition can be improved by using contextual information and a knowledge base to classify segmented image regions.
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.
Dataset Pre-Processing and Artificial Augmentation, Network Architecture and ...INFOGAIN PUBLICATION
Training a Convolutional Neural Network (CNN) based classifier is dependent on a large number of factors. These factors involve tasks such as aggregation of apt dataset, arriving at a suitable CNN network, processing of the dataset, and selecting the training parameters to arrive at the desired classification results. This review includes pre-processing techniques and dataset augmentation techniques used in various CNN based classification researches. In many classification problems, it is usually observed that the quality of dataset is responsible for proper training of CNN network, and this quality is judged on the basis of variations in data for every class. It is not usual to find such a pre-made dataset due to many natural concerns. Also it is recommended to have a large dataset, which is again not usually made available directly as a dataset. In some cases, the noise present in the dataset may not prove useful for training, while in others, researchers prefer to add noise to certain images to make the network less vulnerable to unwanted variations. Hence, researchers use artificial digital imaging techniques to derive variations in the dataset and clear or add noise. Thus, the presented paper accumulates state-of-the-art works that used the pre-processing and artificial augmentation of dataset before training. The next part to data augmentation is training, which includes proper selection of several parameters and a suitable CNN architecture. This paper also includes such network characteristics, dataset characteristics and training methodologies used in biomedical imaging, vision modules of autonomous driverless cars, and a few general vision based applications.
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.
A Survey On: Content Based Image Retrieval Systems Using Clustering Technique...IJMIT JOURNAL
This document summarizes various content-based image retrieval techniques using clustering methods for large datasets. It discusses clustering algorithms like K-means, hierarchical clustering, graph-based clustering and a proposed hybrid divide-and-conquer K-means method. The hybrid method uses hierarchical and divide-and-conquer approaches to improve K-means performance for high dimensional datasets. Content-based image retrieval relies on automatically extracted visual features like color, texture and shape for image classification and retrieval.
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
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.
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-Survey of Image Object Recognition TechniquesIRJET Journal
This document discusses various techniques for object recognition in digital images. It begins by defining object recognition and describing its goals. It then outlines several important techniques for object recognition, including spatial relations, temporal relations, data retrieval in conventional databases, image extraction through mining, and content-based retrieval. For each technique, it provides examples and discusses how the technique can be used to recognize objects in images. The document concludes that object recognition can be improved by using contextual information and a knowledge base to classify segmented image regions.
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.
Dataset Pre-Processing and Artificial Augmentation, Network Architecture and ...INFOGAIN PUBLICATION
Training a Convolutional Neural Network (CNN) based classifier is dependent on a large number of factors. These factors involve tasks such as aggregation of apt dataset, arriving at a suitable CNN network, processing of the dataset, and selecting the training parameters to arrive at the desired classification results. This review includes pre-processing techniques and dataset augmentation techniques used in various CNN based classification researches. In many classification problems, it is usually observed that the quality of dataset is responsible for proper training of CNN network, and this quality is judged on the basis of variations in data for every class. It is not usual to find such a pre-made dataset due to many natural concerns. Also it is recommended to have a large dataset, which is again not usually made available directly as a dataset. In some cases, the noise present in the dataset may not prove useful for training, while in others, researchers prefer to add noise to certain images to make the network less vulnerable to unwanted variations. Hence, researchers use artificial digital imaging techniques to derive variations in the dataset and clear or add noise. Thus, the presented paper accumulates state-of-the-art works that used the pre-processing and artificial augmentation of dataset before training. The next part to data augmentation is training, which includes proper selection of several parameters and a suitable CNN architecture. This paper also includes such network characteristics, dataset characteristics and training methodologies used in biomedical imaging, vision modules of autonomous driverless cars, and a few general vision based applications.
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.
A Survey On: Content Based Image Retrieval Systems Using Clustering Technique...IJMIT JOURNAL
This document summarizes various content-based image retrieval techniques using clustering methods for large datasets. It discusses clustering algorithms like K-means, hierarchical clustering, graph-based clustering and a proposed hybrid divide-and-conquer K-means method. The hybrid method uses hierarchical and divide-and-conquer approaches to improve K-means performance for high dimensional datasets. Content-based image retrieval relies on automatically extracted visual features like color, texture and shape for image classification and retrieval.
The document summarizes an automatic text extraction system for complex images. The system uses discrete wavelet transform for text localization. Morphological operations like erosion and dilation are used to enhance text identification and segmentation. Text regions are segmented using connected component analysis and properties like area and bounding box shape. The extracted text is recognized and shown in a text file. The system allows modifying the recognized text and shows better performance than existing techniques.
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 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.
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.
Facial image retrieval on semantic features using adaptive mean genetic algor...TELKOMNIKA JOURNAL
The emergence of larger databases has made image retrieval techniques an essential component and has led to the development of more efficient image retrieval systems. Retrieval can either be content or text-based. In this paper, the focus is on the content-based image retrieval from the FGNET database. Input query images are subjected to several processing techniques in the database before computing the squared Euclidean distance (SED) between them. The images with the shortest Euclidean distance are considered as a match and are retrieved. The processing techniques involve the application of the median modified Weiner filter (MMWF), extraction of the low-level features using histogram-oriented gradients (HOG), discrete wavelet transform (DWT), GIST, and Local tetra pattern (LTrP). Finally, the features are selected using Adaptive Mean Genetic Algorithm (AMGA). In this study, the average PSNR value obtained after applying the Wiener filter was 45.29. The performance of the AMGA was evaluated based on its precision, F-measure, and recall, and the obtained average values were respectively 0.75, 0.692, and 0.66. The performance matrix of the AMGA was compared to those of particle swarm optimization algorithm (PSO) and genetic algorithm (GA) and found to perform better; thus, proving its efficiency.
This document discusses optimizing content-based image retrieval in peer-to-peer systems. It summarizes previous work on content-based image retrieval using multi-instance queries in peer-to-peer networks. The authors propose two optimizations to previous work: 1) clustering peers to reduce search time, and 2) constructing a search index at cluster heads to avoid searching each peer. Their experiments show the proposed approach reduces search time compared to previous work, with some reduction in accuracy that improves as more nodes are added. The authors plan future work to analyze performance with different cluster sizes and representations for faster search.
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
Analysis of combined approaches of CBIR systems by clustering at varying prec...IJECEIAES
The image retrieving system is used to retrieve images from the image database. Two types of Image retrieval techniques are commonly used: content-based and text-based techniques. One of the well-known image retrieval techniques that extract the images in an unsupervised way, known as the cluster-based image retrieval technique. In this cluster-based image retrieval, all visual features of an image are combined to find a better retrieval rate and precisions. The objectives of the study were to develop a new model by combining the three traits i.e., color, shape, and texture of an image. The color-shape and colortexture models were compared to a threshold value with various precision levels. A union was formed of a newly developed model with a color-shape, and color-texture model to find the retrieval rate in terms of precisions of the image retrieval system. The results were experimented on on the COREL standard database and it was found that the union of three models gives better results than the image retrieval from the individual models. The newly developed model and the union of the given models also gives better results than the existing system named clusterbased retrieval of images by unsupervised learning (CLUE).
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.
A Review on Matching For Sketch TechniqueIOSR Journals
This document summarizes several techniques for sketch-based image retrieval. It discusses methods using SIFT features, HOG descriptors, color segmentation, and gradient orientation histograms. It also reviews applications of these techniques to domains like facial recognition, graffiti matching, and tattoo identification for law enforcement. The techniques aim to extract visual features from sketches that can be used to match and retrieve similar images from databases. While achieving good results, the methods have limitations regarding database size and specificity, and accuracy with complex textures and shapes. Overall, the review examines advances in using sketches as queries for image retrieval.
This document presents a new method for image compression called Haar Wavelet Based Joint Compression Method Using Adaptive Fractal Image Compression (DWT+AFIC). It combines discrete wavelet transform with an existing adaptive fractal image compression technique to improve compression ratio and reconstructed image quality compared to previous fractal image compression methods. The document introduces fractal image compression and its limitations, describes the proposed DWT+AFIC method and 5 other compression techniques, provides simulation results on test images showing DWT+AFIC achieves higher peak signal to noise ratios and compression ratios than other methods, and concludes DWT+AFIC decreases encoding time while increasing compression ratio and maintaining reconstructed image quality.
This document discusses various techniques for image retrieval, including text-based, content-based, and hybrid approaches. Content-based image retrieval (CBIR) extracts visual features like color, texture, shape from images and is able to retrieve similar images to a query image. CBIR systems segment images, extract features, search databases, and return results. CBIR techniques are improving but challenges remain around reducing the semantic gap between low-level features and high-level concepts. Future areas of research include developing techniques more aligned with human perception and improving efficiency and interfaces.
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.
This document discusses image mining techniques for image retrieval. It provides an overview of the image mining process which involves processing images, extracting features, and mining for information and knowledge. The document then surveys various feature extraction techniques used in image mining, including color, texture, and shape features. It discusses how features like color histograms, textures, and invariant moments can be extracted from images and used for content-based image retrieval. Finally, the document reviews several papers on image mining techniques and how they extract different features from images for applications like digital forensics and image retrieval.
Comprehensive Performance Comparison of Cosine, Walsh, Haar, Kekre, Sine, Sla...CSCJournals
This document presents a comparison of various image transforms for content-based image retrieval (CBIR) using fractional coefficients of transformed images. It proposes CBIR techniques that extract features from images transformed using discrete cosine, Walsh, Haar, Kekre, discrete sine, slant, and discrete Hartley transforms. Features are extracted from the gray-scale and individual color planes of images. Fractional coefficients, representing percentages of the full transformed image, are used to reduce feature vector size and speed up retrieval times compared to using the full transform. The techniques are tested on a database of 1000 images from 11 categories, and results show the Kekre transform achieves the best precision and recall, outperforming other transforms when using 6
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.
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.
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.
Applications of spatial features in cbir a surveycsandit
With advances in the computer technology and the World Wide Web there has been an
explosion in the amount and complexity of multimedia data that are generated, stored,
transmitted, analyzed, and accessed. In order to extract useful information from this huge
amount of data, many content based image retrieval (CBIR) systems have been developed in the
last decade. A typical CBIR system captures image features that represent image properties
such as color, texture, or shape of objects in the query image and try to retrieve images from the
database with similar features. Retrieval efficiency and accuracy are the important issues in
designing Content Based Image Retrieval System. The Shape and Spatial features are quiet easy
and simple to derive and effective. Researchers are moving towards finding spatial features and
the scope of implementing these features in to the image retrieval framework for reducing the
semantic gap. This Survey paper focuses on the detailed review of different methods and their
evaluation techniques used in the recent works based on spatial features in CBIR systems.
Finally, several recommendations for future research directions have been suggested based on
the recent technologies.
APPLICATIONS OF SPATIAL FEATURES IN CBIR : A SURVEYcscpconf
This document summarizes research on using spatial features for content-based image retrieval (CBIR). It first discusses common CBIR techniques like feature extraction, selection, and similarity measurement. It then reviews several related works that extract spatial features like edge histograms and color difference histograms. Experimental results show integrating spatial information through image partitioning can improve semantic concept detection performance. While finer partitions carry more spatial data, coarser partitions like 2x2 are preferred to avoid feature mismatch. Future work may explore combining multiple feature domains and contexts to further enhance retrieval accuracy and effectiveness for large-scale image datasets.
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 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.
The document summarizes an automatic text extraction system for complex images. The system uses discrete wavelet transform for text localization. Morphological operations like erosion and dilation are used to enhance text identification and segmentation. Text regions are segmented using connected component analysis and properties like area and bounding box shape. The extracted text is recognized and shown in a text file. The system allows modifying the recognized text and shows better performance than existing techniques.
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 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.
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.
Facial image retrieval on semantic features using adaptive mean genetic algor...TELKOMNIKA JOURNAL
The emergence of larger databases has made image retrieval techniques an essential component and has led to the development of more efficient image retrieval systems. Retrieval can either be content or text-based. In this paper, the focus is on the content-based image retrieval from the FGNET database. Input query images are subjected to several processing techniques in the database before computing the squared Euclidean distance (SED) between them. The images with the shortest Euclidean distance are considered as a match and are retrieved. The processing techniques involve the application of the median modified Weiner filter (MMWF), extraction of the low-level features using histogram-oriented gradients (HOG), discrete wavelet transform (DWT), GIST, and Local tetra pattern (LTrP). Finally, the features are selected using Adaptive Mean Genetic Algorithm (AMGA). In this study, the average PSNR value obtained after applying the Wiener filter was 45.29. The performance of the AMGA was evaluated based on its precision, F-measure, and recall, and the obtained average values were respectively 0.75, 0.692, and 0.66. The performance matrix of the AMGA was compared to those of particle swarm optimization algorithm (PSO) and genetic algorithm (GA) and found to perform better; thus, proving its efficiency.
This document discusses optimizing content-based image retrieval in peer-to-peer systems. It summarizes previous work on content-based image retrieval using multi-instance queries in peer-to-peer networks. The authors propose two optimizations to previous work: 1) clustering peers to reduce search time, and 2) constructing a search index at cluster heads to avoid searching each peer. Their experiments show the proposed approach reduces search time compared to previous work, with some reduction in accuracy that improves as more nodes are added. The authors plan future work to analyze performance with different cluster sizes and representations for faster search.
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
Analysis of combined approaches of CBIR systems by clustering at varying prec...IJECEIAES
The image retrieving system is used to retrieve images from the image database. Two types of Image retrieval techniques are commonly used: content-based and text-based techniques. One of the well-known image retrieval techniques that extract the images in an unsupervised way, known as the cluster-based image retrieval technique. In this cluster-based image retrieval, all visual features of an image are combined to find a better retrieval rate and precisions. The objectives of the study were to develop a new model by combining the three traits i.e., color, shape, and texture of an image. The color-shape and colortexture models were compared to a threshold value with various precision levels. A union was formed of a newly developed model with a color-shape, and color-texture model to find the retrieval rate in terms of precisions of the image retrieval system. The results were experimented on on the COREL standard database and it was found that the union of three models gives better results than the image retrieval from the individual models. The newly developed model and the union of the given models also gives better results than the existing system named clusterbased retrieval of images by unsupervised learning (CLUE).
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.
A Review on Matching For Sketch TechniqueIOSR Journals
This document summarizes several techniques for sketch-based image retrieval. It discusses methods using SIFT features, HOG descriptors, color segmentation, and gradient orientation histograms. It also reviews applications of these techniques to domains like facial recognition, graffiti matching, and tattoo identification for law enforcement. The techniques aim to extract visual features from sketches that can be used to match and retrieve similar images from databases. While achieving good results, the methods have limitations regarding database size and specificity, and accuracy with complex textures and shapes. Overall, the review examines advances in using sketches as queries for image retrieval.
This document presents a new method for image compression called Haar Wavelet Based Joint Compression Method Using Adaptive Fractal Image Compression (DWT+AFIC). It combines discrete wavelet transform with an existing adaptive fractal image compression technique to improve compression ratio and reconstructed image quality compared to previous fractal image compression methods. The document introduces fractal image compression and its limitations, describes the proposed DWT+AFIC method and 5 other compression techniques, provides simulation results on test images showing DWT+AFIC achieves higher peak signal to noise ratios and compression ratios than other methods, and concludes DWT+AFIC decreases encoding time while increasing compression ratio and maintaining reconstructed image quality.
This document discusses various techniques for image retrieval, including text-based, content-based, and hybrid approaches. Content-based image retrieval (CBIR) extracts visual features like color, texture, shape from images and is able to retrieve similar images to a query image. CBIR systems segment images, extract features, search databases, and return results. CBIR techniques are improving but challenges remain around reducing the semantic gap between low-level features and high-level concepts. Future areas of research include developing techniques more aligned with human perception and improving efficiency and interfaces.
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.
This document discusses image mining techniques for image retrieval. It provides an overview of the image mining process which involves processing images, extracting features, and mining for information and knowledge. The document then surveys various feature extraction techniques used in image mining, including color, texture, and shape features. It discusses how features like color histograms, textures, and invariant moments can be extracted from images and used for content-based image retrieval. Finally, the document reviews several papers on image mining techniques and how they extract different features from images for applications like digital forensics and image retrieval.
Comprehensive Performance Comparison of Cosine, Walsh, Haar, Kekre, Sine, Sla...CSCJournals
This document presents a comparison of various image transforms for content-based image retrieval (CBIR) using fractional coefficients of transformed images. It proposes CBIR techniques that extract features from images transformed using discrete cosine, Walsh, Haar, Kekre, discrete sine, slant, and discrete Hartley transforms. Features are extracted from the gray-scale and individual color planes of images. Fractional coefficients, representing percentages of the full transformed image, are used to reduce feature vector size and speed up retrieval times compared to using the full transform. The techniques are tested on a database of 1000 images from 11 categories, and results show the Kekre transform achieves the best precision and recall, outperforming other transforms when using 6
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.
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.
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.
Applications of spatial features in cbir a surveycsandit
With advances in the computer technology and the World Wide Web there has been an
explosion in the amount and complexity of multimedia data that are generated, stored,
transmitted, analyzed, and accessed. In order to extract useful information from this huge
amount of data, many content based image retrieval (CBIR) systems have been developed in the
last decade. A typical CBIR system captures image features that represent image properties
such as color, texture, or shape of objects in the query image and try to retrieve images from the
database with similar features. Retrieval efficiency and accuracy are the important issues in
designing Content Based Image Retrieval System. The Shape and Spatial features are quiet easy
and simple to derive and effective. Researchers are moving towards finding spatial features and
the scope of implementing these features in to the image retrieval framework for reducing the
semantic gap. This Survey paper focuses on the detailed review of different methods and their
evaluation techniques used in the recent works based on spatial features in CBIR systems.
Finally, several recommendations for future research directions have been suggested based on
the recent technologies.
APPLICATIONS OF SPATIAL FEATURES IN CBIR : A SURVEYcscpconf
This document summarizes research on using spatial features for content-based image retrieval (CBIR). It first discusses common CBIR techniques like feature extraction, selection, and similarity measurement. It then reviews several related works that extract spatial features like edge histograms and color difference histograms. Experimental results show integrating spatial information through image partitioning can improve semantic concept detection performance. While finer partitions carry more spatial data, coarser partitions like 2x2 are preferred to avoid feature mismatch. Future work may explore combining multiple feature domains and contexts to further enhance retrieval accuracy and effectiveness for large-scale image datasets.
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 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.
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 review of different techniques for image retrieval from large databases, including text-based image retrieval and content-based image retrieval (CBIR). CBIR uses visual features extracted from images like color, texture, and shape to search for similar images. The document discusses some limitations of CBIR and proposes video-based image retrieval as a new direction. It also surveys recent research in areas like feature extraction, indexing, and discusses future directions like reducing the semantic gap between low-level features and high-level meanings.
Content Based Image Retrieval: A ReviewIRJET Journal
This document reviews content-based image retrieval (CBIR) techniques. It discusses how CBIR systems extract features like color, texture, and shape from images to enable search and retrieval of similar images from a database. Color features may use color histograms in color spaces like RGB. Texture features can use techniques like Gabor wavelet transforms and Tamura features. Shape is often extracted using edge detection methods. The document outlines the general CBIR workflow of feature extraction, matching, and retrieval. It also reviews several existing CBIR methods and techniques used for feature extraction.
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.
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.
A deep locality-sensitive hashing approach for achieving optimal image retri...IJECEIAES
The proposed method achieves optimal image retrieval through a deep locality-sensitive hashing approach. It extracts both low-level visual features and high-level semantic features from different layers of a CNN model. Locality-sensitive hashing is applied to the features to generate hash codes, which are then used to quickly retrieve similar images based on Hamming distance. Experimental results on CIFAR-10 and NUS-WIDE datasets show the proposed method outperforms other hash-based image retrieval methods in terms of accuracy and retrieval time.
This document discusses various techniques for image retrieval, including text-based, content-based, and hybrid approaches. Content-based image retrieval (CBIR) extracts visual features like color, texture, shape from images and is able to retrieve similar images to a query image. CBIR systems segment images, extract features, search databases, and return results. CBIR has advantages over text-based retrieval but challenges remain around the semantic gap between low-level features and high-level concepts. The document also discusses evaluating retrieval performance and promising future research directions like reducing the semantic gap.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
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Content-based image retrieval based on corel dataset using deep learningIAESIJAI
A popular technique for retrieving images from huge and unlabeled image databases are content-based-image-retrieval (CBIR). However, the traditional information retrieval techniques do not satisfy users in terms of time consumption and accuracy. Additionally, the number of images accessible to users are growing due to web development and transmission networks. As the result, huge digital image creation occurs in many places. Therefore, quick access to these huge image databases and retrieving images like a query image from these huge image collections provides significant challenges and the need for an effective technique. Feature extraction and similarity measurement are important for the performance of a CBIR technique. This work proposes a simple but efficient deep-learning framework based on convolutional-neural networks (CNN) for the feature extraction phase in CBIR. The proposed CNN aims to reduce the semantic gap between low-level and high-level features. The similarity measurements are used to compute the distance between the query and database image features. When retrieving the first 10 pictures, an experiment on the Corel-1K dataset showed that the average precision was 0.88 with Euclidean distance, which was a big step up from the state-of-the-art approaches.
IRJET- Efficient Auto Annotation for Tag and Image based Searching Over Large...IRJET Journal
This document discusses an efficient auto annotation system for tag and image based searching over large datasets. It proposes an algorithm that incorporates advantages from other algorithms to improve accuracy and performance of image retrieval in a peer-to-peer framework. Key features extracted for image matching include color, shape, and texture. Experimental results on a dataset of 500 images showed the method achieved 92.4% accuracy in retrieving similar images, comparable to other methods. The system allows users to recognize and extract images across peer systems based on image features.
Content-based image retrieval (CBIR) uses the content features for
retrieving and searching the images in a given large database. Earlier,
different hand feature descriptor designs are researched based on cues that
are visual such as shape, colour, and texture used to represent these images.
Although, deep learning technologies have widely been applied as an
alternative to designing engineering that is dominant for over a decade. The
features are automatically learnt through the data. This research work
proposes integrated dual deep convolutional neural network (IDD-CNN),
IDD-CNN comprises two distinctive CNN, first CNN exploits the features
and further custom CNN is designed for exploiting the custom features.
Moreover, a novel directed graph is designed that comprises the two blocks
i.e. learning block and memory block which helps in finding the similarity
among images; since this research considers the large dataset, an optimal
strategy is introduced for compact features. Moreover, IDD-CNN is
evaluated considering the two distinctive benchmark datasets the oxford
dataset considering mean average precision (mAP) metrics and comparative
analysis shows IDD-CNN outperforms the other existing model.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
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.
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.
IRJET- A Survey on Different Image Retrieval TechniquesIRJET Journal
This document discusses different techniques for content-based image retrieval. It begins by describing content-based image retrieval (CBIR) and how it uses visual features like color, texture, and shape to search for images, unlike text-based retrieval which relies on metadata. It then discusses various CBIR techniques in detail, focusing on block truncation coding (BTC) techniques. Specifically, it examines dot diffusion block truncation coding (DDBTC), which extracts color histogram and bit pattern features to retrieve images. Performance is measured using average precision and recall rates.
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This document describes an electronic doorbell system that uses a keypad and GSM for home security. The system consists of a doorbell connected to a microcontroller that triggers a GSM module to send an SMS to the homeowner when the doorbell is pressed. The homeowner can then respond via button press to open the door, or a message will be displayed if they do not respond. An authorized person can enter a password on the keypad, and if multiple wrong passwords are entered, a message will be sent to the homeowner about a potential burglary attempt. The system aims to provide notification to homeowners and prevent unauthorized access through use of passwords and messaging capabilities.
Augmented reality, the new age technology, has widespread applications in every field imaginable. This technology has proven to be an inflection point in numerous verticals, improving lives and improving performance. In this paper, we explore the various possible applications of Augmented Reality (AR) in the field of Medicine. The objective of using AR in medicine or generally in any field is the fact that, AR helps in motivating the user, making sessions interactive and assist in faster learning. In this paper, we discuss about the applicability of AR in the field of medical diagnosis. Augmented reality technology reinforces remote collaboration, allowing doctors to diagnose patients from a different locality. Additionally, we believe that a much more pronounced effect can be achieved by bringing together the cutting edge technology of AR and the lifesaving field of Medical sciences. AR is a mechanism that could be applied in the learning process too. Similarly, virtual reality could be used in the field where more of practical experience is needed such as driving, sports, neonatal care training.
Image fusion is a sub field of image processing in which more than one images are fused to create an image where all the objects are in focus. The process of image fusion is performed for multi-sensor and multi-focus images of the same scene. Multi-sensor images of the same scene are captured by different sensors whereas multi-focus images are captured by the same sensor. In multi-focus images, the objects in the scene which are closer to the camera are in focus and the farther objects get blurred. Contrary to it, when the farther objects are focused then closer objects get blurred in the image. To achieve an image where all the objects are in focus, the process of images fusion is performed either in spatial domain or in transformed domain. In recent times, the applications of image processing have grown immensely. Usually due to limited depth of field of optical lenses especially with greater focal length, it becomes impossible to obtain an image where all the objects are in focus. Thus, it plays an important role to perform other tasks of image processing such as image segmentation, edge detection, stereo matching and image enhancement. Hence, a novel feature-level multi-focus image fusion technique has been proposed which fuses multi-focus images. Thus, the results of extensive experimentation performed to highlight the efficiency and utility of the proposed technique is presented. The proposed work further explores comparison between fuzzy based image fusion and neuro fuzzy fusion technique along with quality evaluation indices.
Graphs have become the dominant life-form of many tasks as they advance a
structure to represent many tasks and the corresponding relations. A powerful
role of networks/graphs is to bridge local feats that exist in vertices as they
blossom into patterns that help explain how nodal relations and their edges
impacts a complex effect that ripple via a graph. User cluster are formed as a
result of interactions between entities. Many users can hardly categorize their
contact into groups today such as “family”, “friends”, “colleagues” etc. Thus,
the need to analyze such user social graph via implicit clusters, enables the
dynamism in contact management. Study seeks to implement this dynamism
via a comparative study of deep neural network and friend suggest algorithm.
We analyze a user’s implicit social graph and seek to automatically create
custom contact groups using metrics that classify such contacts based on a
user’s affinity to contacts. Experimental results demonstrate the importance
of both the implicit group relationships and the interaction-based affinity in
suggesting friends.
This paper projects Gryllidae Optimization Algorithm (GOA) has been applied to solve optimal reactive power problem. Proposed GOA approach is based on the chirping characteristics of Gryllidae. In common, male Gryllidae chirp, on the other hand some female Gryllidae also do as well. Male Gryllidae draw the females by this sound which they produce. Moreover, they caution the other Gryllidae against dangers with this sound. The hearing organs of the Gryllidae are housed in an expansion of their forelegs. Through this, they bias to the produced fluttering sounds. Proposed Gryllidae Optimization Algorithm (GOA) has been tested in standard IEEE 14, 30 bus test systems and simulation results show that the projected algorithms reduced the real power loss considerably.
In the wake of the sudden replacement of wood and kerosene by gas cookers for several purposes in Nigeria, gas leakage has caused several damages in our homes, Laboratories among others. installation of a gas leakage detection device was globally inspired to eliminate accidents related to gas leakage. We present an alternative approach to developing a device that can automatically detect and control gas leakages and also monitor temperature. The system detects the leakage of the LPG (Liquefied Petroleum Gas) using a gas sensor, then triggred the control system response which employs ventilator system, Mobile phone alert and alarm when the LPG concentration in the air exceeds a certain level. The performance of two gas sensors (MQ5 and MQ6) were tested for a guided decision. Also, when the temperature of the environment poses a danger, LED (indicator), buzzer and LCD (16x2) display was used to indicate temperature and gas leakage status in degree Celsius and PPM respectively. Attension was given to the response time of the control system, which was ascertained that this system significantly increases the chances and efficiency of eliminating gas leakage related accident.
Feature selection problem is one of the main important problems in the text and data mining domain. This paper presents a comparative study of feature selection methods for Arabic text classification. Five of the feature selection methods were selected: ICHI square, CHI square, Information Gain, Mutual Information and Wrapper. It was tested with five classification algorithms: Bayes Net, Naive Bayes, Random Forest, Decision Tree and Artificial Neural Networks. In addition, Data Collection was used in Arabic consisting of 9055 documents, which were compared by four criteria: Precision, Recall, F-measure and Time to build model. The results showed that the improved ICHI feature selection got almost all the best results in comparison with other methods.
The document proposes the Gentoo Penguin Algorithm (GPA) to solve the optimal reactive power problem. The goal is to minimize real power loss. GPA is inspired by the natural behaviors of Gentoo penguins. In GPA, penguin positions represent potential solutions. Penguins move toward other penguins with lower "cost" or higher heat concentration, representing better solutions. Cost is defined by heat concentration and distance between penguins. Heat radiation decreases with distance. The algorithm is tested on the IEEE 57 bus system and reduces real power loss effectively compared to other methods.
08 20272 academic insight on applicationIAESIJEECS
This research has thrown up many questions in need of further investigation.There was an expressive quantitative-qualitative research, which a common investigation form was used in.The dialogue item was also applied to discover if the contributors asserted the media-based attitude supplements their learning of academic English writing classes or not.Data recounted academic” insights toward using Skype as a sustaining implement for lessons releasing based on chosen variables: their occupation, year of education, and knowledge with Skype discovered that there were no important statistical differences in the use of Skype units because of medical academics major knowledge. There are statistically important differences in using Skype units. The findings also, disclosed that there are statistically significant differences in using Skype units due to the practice with Skype variable, in favors of academics with no Skype use practice. Skype instrument as an instructive media is a positive medium to be employed to supply academic medical writing data and assist education. Academics who do not have enough time to contribute in classes believe comfortable using the Skype-based attitude in scientific writing. They who took part in the course claimed that their approval of this media is due to learning academic innovative medical writing.
Cloud computing has sweeping impact on the human productivity. Today it’s used for Computing, Storage, Predictions and Intelligent Decision Making, among others. Intelligent Decision-Making using Machine Learning has pushed for the Cloud Services to be even more fast, robust and accurate. Security remains one of the major concerns which affect the cloud computing growth however there exist various research challenges in cloud computing adoption such as lack of well managed service level agreement (SLA), frequent disconnections, resource scarcity, interoperability, privacy, and reliability. Tremendous amount of work still needs to be done to explore the security challenges arising due to widespread usage of cloud deployment using Containers. We also discuss Impact of Cloud Computing and Cloud Standards. Hence in this research paper, a detailed survey of cloud computing, concepts, architectural principles, key services, and implementation, design and deployment challenges of cloud computing are discussed in detail and important future research directions in the era of Machine Learning and Data Science have been identified.
Notary is an official authorized to make an authentic deed regarding all deeds, agreements and stipulations required by a general rule. Activities carried out at the notary office such as recording client data and file data still use traditional systems that tend to be manual. The problem that occurs is the inefficiency in data processing and providing information to clients. Clients have difficulty getting information related to the progress of documents that are being taken care of at the notary's office. The client must take the time to arrive to the notary's office repeatedly to check the progress of the work of the document file. The purpose of this study is to facilitate clients in obtaining information about the progress of the work in progress, and make it easier for employees to process incoming documents by implementing an administrative system. This system was developed with the waterfall system development method and uses the Multi-Channel Access Technology integrated in the website to simplify the process of delivering information and requesting information from clients and to clients with Telegram and SMS Gateway. Clients will come to the office only when there is a notification from the system via Telegram or SMS notifying that the client must come directly to the notary's office, thus leading to an efficient time and avoiding excessive transportation costs. The overall functional system can function properly based on the results of alpha testing. The results of beta testing conducted by distributing the system feasibility test questionnaire to end users, get a percentage of 96% of users agree the system is feasible to be implemented.
In this work Tundra wolf algorithm (TWA) is proposed to solve the optimal reactive power problem. In the projected Tundra wolf algorithm (TWA) in order to avoid the searching agents from trapping into the local optimal the converging towards global optimal is divided based on two different conditions. In the proposed Tundra wolf algorithm (TWA) omega tundra wolf has been taken as searching agent as an alternative of indebted to pursue the first three most excellent candidates. Escalating the searching agents’ numbers will perk up the exploration capability of the Tundra wolf wolves in an extensive range. Proposed Tundra wolf algorithm (TWA) has been tested in standard IEEE 14, 30 bus test systems and simulation results show the proposed algorithm reduced the real power loss effectively.
In this work Predestination of Particles Wavering Search (PPS) algorithm has been applied to solve optimal reactive power problem. PPS algorithm has been modeled based on the motion of the particles in the exploration space. Normally the movement of the particle is based on gradient and swarming motion. Particles are permitted to progress in steady velocity in gradient-based progress, but when the outcome is poor when compared to previous upshot, immediately particle rapidity will be upturned with semi of the magnitude and it will help to reach local optimal solution and it is expressed as wavering movement. In standard IEEE 14, 30, 57,118,300 bus systems Proposed Predestination of Particles Wavering Search (PPS) algorithm is evaluated and simulation results show the PPS reduced the power loss efficiently.
In this paper, Mine Blast Algorithm (MBA) has been intermingled with Harmony Search (HS) algorithm for solving optimal reactive power dispatch problem. MBA is based on explosion of landmines and HS is based on Creativeness progression of musicians-both are hybridized to solve the problem. In MBA Initial distance of shrapnel pieces are reduced gradually to allow the mine bombs search the probable global minimum location in order to amplify the global explore capability. Harmony search (HS) imitates the music creativity process where the musicians supervise their instruments’ pitch by searching for a best state of harmony. Hybridization of Mine Blast Algorithm with Harmony Search algorithm (MH) improves the search effectively in the solution space. Mine blast algorithm improves the exploration and harmony search algorithm augments the exploitation. At first the proposed algorithm starts with exploration & gradually it moves to the phase of exploitation. Proposed Hybridized Mine Blast Algorithm with Harmony Search algorithm (MH) has been tested on standard IEEE 14, 300 bus test systems. Real power loss has been reduced considerably by the proposed algorithm. Then Hybridized Mine Blast Algorithm with Harmony Search algorithm (MH) tested in IEEE 30, bus system (with considering voltage stability index)- real power loss minimization, voltage deviation minimization, and voltage stability index enhancement has been attained.
Artificial Neural Networks have proved their efficiency in a large number of research domains. In this paper, we have applied Artificial Neural Networks on Arabic text to prove correct language modeling, text generation, and missing text prediction. In one hand, we have adapted Recurrent Neural Networks architectures to model Arabic language in order to generate correct Arabic sequences. In the other hand, Convolutional Neural Networks have been parameterized, basing on some specific features of Arabic, to predict missing text in Arabic documents. We have demonstrated the power of our adapted models in generating and predicting correct Arabic text comparing to the standard model. The model had been trained and tested on known free Arabic datasets. Results have been promising with sufficient accuracy.
In the present-day communications speech signals get contaminated due to
various sorts of noises that degrade the speech quality and adversely impacts
speech recognition performance. To overcome these issues, a novel approach
for speech enhancement using Modified Wiener filtering is developed and
power spectrum computation is applied for degraded signal to obtain the
noise characteristics from a noisy spectrum. In next phase, MMSE technique
is applied where Gaussian distribution of each signal i.e. original and noisy
signal is analyzed. The Gaussian distribution provides spectrum estimation
and spectral coefficient parameters which can be used for probabilistic model
formulation. Moreover, a-priori-SNR computation is also incorporated for
coefficient updation and noise presence estimation which operates similar to
the conventional VAD. However, conventional VAD scheme is based on the
hard threshold which is not capable to derive satisfactory performance and a
soft-decision based threshold is developed for improving the performance of
speech enhancement. An extensive simulation study is carried out using
MATLAB simulation tool on NOIZEUS speech database and a comparative
study is presented where proposed approach is proved better in comparison
with existing technique.
Previous research work has highlighted that neuro-signals of Alzheimer’s disease patients are least complex and have low synchronization as compared to that of healthy and normal subjects. The changes in EEG signals of Alzheimer’s subjects start at early stage but are not clinically observed and detected. To detect these abnormalities, three synchrony measures and wavelet-based features have been computed and studied on experimental database. After computing these synchrony measures and wavelet features, it is observed that Phase Synchrony and Coherence based features are able to distinguish between Alzheimer’s disease patients and healthy subjects. Support Vector Machine classifier is used for classification giving 94% accuracy on experimental database used. Combining, these synchrony features and other such relevant features can yield a reliable system for diagnosing the Alzheimer’s disease.
Attenuation correction designed for PET/MR hybrid imaging frameworks along with portion making arrangements used for MR-based radiation treatment remain testing because of lacking high-energy photon weakening data. We present a new method so as to uses the learned nonlinear neighborhood descriptors also highlight coordinating toward foresee pseudo-CT pictures starting T1w along with T2w MRI information. The nonlinear neighborhood descriptors are acquired through anticipating the direct descriptors interested in the nonlinear high-dimensional space utilizing an unequivocal constituent guide also low-position guess through regulated complex regularization. The nearby neighbors of every near descriptor inside the data MR pictures are looked during an obliged spatial extent of the MR pictures among the training dataset. By that point, the pseudo-CT patches are evaluated through k-closest neighbor relapse. The planned procedure designed for pseudo-CT forecast is quantitatively broke downward on top of a dataset comprising of coordinated mind MRI along with CT pictures on or after 13 subjects.
The cognitive radio prototype performance is to alleviate the scarcity of spectral resources for wireless communication through intelligent sensing and quick resource allocation techniques. Secondary users (SU’s) actively obtain the spectrum access opportunity by supporting primary users (PU’s) in cognitive radio networks (CRNs). In present generation, spectrum access is endowed through cooperative communication-based link-level frame-based cooperative (LLC) principle. In this SUs independently act as conveyors for PUs to achieve spectrum access opportunities. Unfortunately, this LLC approach cannot fully exploit spectrum access opportunities to enhance the throughput of CRNs and fails to motivate PUs to join the spectrum sharing processes. Therefore, to overcome this con, network level cooperative (NLC) principle was used, where SUs are integrated mutually to collaborate with PUs session by session, instead of frame based cooperation for spectrum access opportunities. NLC approach has justified the challenges facing in LLC approach. In this paper we make a survey of some models that have been proposed to tackle the problem of LLC. We show the relevant aspects of each model, in order to characterize the parameters that we should take in account to achieve a spectrum access opportunity.
In this paper, the author provides insights and lessons that can be learned from colleagues at American universities about their online education experiences. The literature review and previous studies of online educations gains are explored and summarized in this research. Emerging trends in online education are discussed in detail, and strategies to implement these trends are explained. The author provides several tools and strategies that enable universities to ensure the quality of online education. At the end of this research paper, the researcher provides examples from Arab universities who have successfully implemented online education and expanded their impact on the society. This research provides a strategy and a model that can be used by universities in the Middle East as a roadmap to implement online education in their regions.
TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEMHODECEDSIET
Time Division Multiplexing (TDM) is a method of transmitting multiple signals over a single communication channel by dividing the signal into many segments, each having a very short duration of time. These time slots are then allocated to different data streams, allowing multiple signals to share the same transmission medium efficiently. TDM is widely used in telecommunications and data communication systems.
### How TDM Works
1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
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.
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Figure 1. Content Based Image Retrieval System
According to Internet Trends 2014 code conference [3], people upload an average of
1.8 billion digital images every single day and that is equivalent to 657 billion photos per year
which is a very huge number. Most of these images are in JPEG compressed format and hence
a direct retreival mechanism in this compressed domain would be better as compared to
decompressing them completely which is a costly process in digital world. In this work, we have
identified the features that can be extracted in the compressed domain on the DCT coefficients
itself and also propose to use genetic algorithms for getting the relevance feedback from the
end user and apply it in fine tuning the retrieved results.
2. Literature Survey
Image content includes both visual and semantic content for its description. It could
also be specific to a particular domain or a generic feature. Specific ones include human face,
finger prints etc. are used in specific application while the generic ones like color and texture are
used in other domains. The challenge involved in designing a robust CBIR system is in
extracting the content descriptors which should be invariant to the issues introduced due to
imaging process.
Researchers from different fields including computer vision, database management,
and information retreival are all attracted to CBIR problem statement [5] from the year 1992.
Also since the year 1997, the different research publications are mostly based on primitive
features extracted from the spatial domain [6] and some had relevance feedback added to it [7].
Efforts were also made to combine the visual features together for an improved and
robust performance of CBIR system [8] but lacked the optimization solution as it takes a huge
amount of time for feature extraction, comparison and retreival process [9]. This made the
researchers to turn towards extracting the features in the compressed domain itself as the huge
amount of data in the internet as well as local storage is in JPEG compressed format [12].
Color is an important feature in judging the image similarity and hence multiple research
papers are published using this feature [17]. Texture is the next commonly used feature [19] as
it helps in identifying the repeated patterns in the image or a video frame. To differentiate
between different objects of the same color, Texture will be very useful. The different texture
descriptors that are usually extracted from the images include mean, standard deviation,
angular second moment, inverse difference moment, sum average, contrast, correlations and
sum variance. These above mentioned descriptors have been used to select on the number of
uniform or textured or coarse blocks present in the image.
Shape is the last used feature in most CBIR systems. In order to find the shape of the
image, different edge detection algorithms [14] are used. They compute the moment of the
query image and integrate the moment features of given images row and column wise [21].
Then they are compared against the database stored images moments and the closest
matching ones are retrieved back.
For two reasons including the overhead in decompressing the images and the fact that
the compressed ones hold the most wanted information by removing the redundant information
during the compression process, there were research works on compressed domain CBIR
systems as well [22]. Zhe-Ming Lu and others [23] feel that the existing CBIR methods are far
from the practical application and have presented a novel approach in extracting the color,
texture and other features on the DCT domain. Their system is found to be suitable for all color
images of different sizes as well.
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Genetic algorithms (GA) exist in the literature and use as well for a while and is a
method for explaining both constrained and unconstrained optimization problems based on a
natural selection process. The algorithm modifies a population repeatedly of individual solutions.
Genetic algorithms along with precision and recall parameters can be used as a fitness function
in selecting the optimum weights of features [24]. It is observed that the optimal weights of
features that are computed by GA have improved meaningfully all the assessment measures
including average precision and recall for the combined features method. Though it is explored
mostly in spatial domain features, we have extended the GA to the features extracted in the
compressed domain and found the results to be even better.
3. Proposed Research Framework
Image Retreival can be classified based on different levels of complexity in extracting
the features as Level 1, Level 2 and Level 3 respectively. Level 1 deals with primitive features
including color, texture and shape while level 2 is based on logical or derived features and level
3 is about retrieval based on abstract attributes. Level 3 takes in to account the semantics,
purpose and content of the image on top of primitive features. Semantic gap deals with the
difference between the user query and the retrieval results. Tougher the feature extraction from
a meaningful data will reduce the semantic gap. For this purpose, we will first extract the
features from the DCT domain which has no redundant information in it.
3.1. JPEG Standard and Content Analysis
JPEG (Joint Photographic Experts Group) is a lossy compression technique used in
storage of digital images. The degree of compression is an adjustable parameter based on the
application and use cases. Moreover it is the most common format used for storing and
transmitting the images on the World Wide Web [25]. So, we consider JPEG images compared
to other compression formats in this research work.
JPEG uses a lossy compression technique based on the Discrete Cosine Transform
(DCT). This transform converts a 2D image in the spatial domain to the frequency domain. As
the human psychovisual system does not give importance to the high frequency information,
this transformation in the process of quantization removes all these redundant information and
stores back only the required details to reproduce the image. Though this method comes under
lossy compression technique, we get the required amount information that is needed for the
content based image retreival system.
The encoding process involves color space transformation, down sampling, block
splitting followed by discrete cosine transformation of the data, quantization, entrophy encoding
and packing. The decoder follows the exact reverse process as shown in Figure 2 and in this
work, we concentrate hugely on DCT part as the feature extraction is surrounding that. The two
dimensional DCT is given by:
In the above equation ‘u’ represents the horizontal spatial frequency, v represents the
vertical spatial frequency. g(x,y) corresponds to pixel value at co-ordinates (x,y) and G(u,v)
represents the DCT coefficients at co-ordinates (u,v).
Figure 2. Feature Extraction from JPEG compressed image
Feature Extraction
JPEG Compressed
Image as input
Huffman decoding and
de-quantization
DCT Features
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At the end of DCT transform, we get 64 frequency components. The left most and the
first DCT coefficient represents the DC component which corresponds to the average luminance
in this block and is the most important information. The other 63 coefficients can be classified as
the low and high frequency components based on their position in the block. Various features
can be extracted from these coefficients which are discussed in the next section.
4. Feature Extraction in the DCT Domain
We extract the features from the JPEG compressed image before proceeding with
image matching and retreival process. In this section we detail the different features considered
in our work for image retrieval. The different features include color and texture information from
the compressed images.
4.1. Color Feature from DCT
Color is important visual information and it was established using the content based
image indexing. So we get the color moments as the first feature vector in our work as well. It is
represented through the first two moments namely the mean and the standard deviation.
Along with the color moments, we also use color histogram for identifying the image
content. This is given by:
h(i) = ni / n
Here ni represents the number of pixels of the ith color and n represents the number of
pixels in the image. We extract and store these feature vectors in the database. We calculate
them independently for every block identified and store all of them.
4.2. Texture Feature from DCT
Texture is the next feature identified for image retreival purposes. The first DC
coefficient in the DCT block represents the energy information of the image while the remaining
AC components indicate the frequency information. The direction information is also present in
the block. The dominant directions along with the gray level variations of the image are
extracted and stored in the image database as independent feature vectors. Each sub block is
processed independently as shown in Figure 3.
Figure 3. Texture Extraction from DCT blocks
The vertical and horizontal information’s are extracted from each block and processed
for further usage. Once all the values are computed, we calculate the mean and standard
deviation over these values and store them. The feature vectors represent the average
greyness, horizontal texture, vertical texture and diagonal texture of a particular image sub
block, respectively. These coefficients are intentionally selected as they are vital to the greyness
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and image directionality. Also, only the low frequency coefficients of the sub block are selected
as they deliver higher energy level in a usual DCT domain.
5. Genetic Algorithm in CBIR Compressed Domain Approach
The difference seen between the user’s need and the images retrieved represents the
semantic gap in CBIR systems. In order to reduce this semantic gap, relevance feedback
mechanisms are introduced. By means of this relevance feedback, we will be able to integrate
human perception in to the query and evaluate the results as well.
Genetic algorithms which is a method for solving optimization problems can thus be
borrowed from biological evolution to relevance feedback mechanism in CBIR systems. Genetic
algorithms simulates the process of evolution and evolution is an optimizing process. Through
this process, the successive generations will become better and better and each generation is
like an iteration in numerical methods. In CBIR system also, we improve the results over every
iteration through relevance feedback from the end user.
Selection, crossover operation and mutation along with acceptance are the different
stages involved in genetic algorithm as depicted in Figure 4. Selection operation is the first in
the list which takes care of selecting the individual feature vectors as parents that can generate
offspring. The selection happens through the fitness level. The better the feature vectors are,
there are more chances for them to be selected. The successor’s generation in a genetic
algorithm is found by a set of operators that recombine and mutate certain members of the
existing population. Crossover and mutation are the two most common operators used for this.
The crossover operator yields two new offspring from two parent strings, by replicating certain
bits from each parent. The mutation on the other hand will make small changes to the bit string
by selecting a random bit and modifying its value.
Figure 4. Genetic Algorithm in refining the CBIR results
The fitness function is obtained by the following equation:
F1 = Rr – Rn – Nr
where Rr is the number of images retrieved that are in the set of relevant images provided by
the database and judged as relevant by the user implicitly without their knowledge. Rn refers to
the number of images retrieved that are not included in the standard relevant set and Nr is the
number of relevant documents that is not retrieved.
The images are sorted now in the order of decreasing similarity and the final fitness is
given by the equation:
Next
Iteration
Select Next generation for processing
Iteration 0
Create a population of matching vectors
Determine the fitness level of each vector
Perform reproduction using cross over and
mutation
Display
Results
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where |D| refers to the total number of images retrieved and r(d) signifies the relevance of the
images d. This will be unity if the image is relevant otherwise zero. By this way, during every
iteration we will be able to filter out the irrelevant images and display back the matching ones.
6. Results and Discussion
One of the main goals of this research was to improve the retreival accuracy of the
CBIR system and find its use in multiple fields. We have used the STL-10 dataset for our
experimentations. It is motivated by the CIFAR-10 dataset but has alterations to it. We have 10
different classes including the airplane, tiger, bird, duck, players, buildings, flowers, ships and
water images in it. We have used 500 images in the database and 25 images for testing. All the
images are of 96*96 pixels and JPEG compressed.
As a first step, we have extracted the color and texture features from the 500 images
that are present in our database and stored them as multidimensional feature vectors. Then we
had a MATLAB implementation of GUI for querying a test image and to display the results. We
started testing the 25 images one by one. Every time a query image is provided, we extract the
same set of features from it and used Euclidean distance method to find the distance between
the extracted feature vector value and the data present in the database. We then used precision
and recall parameters for estimating our retrieval accuracy.
As a final step, we introduced the Genetic algorithms in getting the relevance feedback
from the end user and used it in fine tuning the retreival results. It is clear from the results that
the retrieval accuracy increases by 12% when we use relevance feedback and genetic
algorithms in compressed domain content based image retreival systems. The data set along
with the retrieved images are illustrated in Figure 5 while the results are tabulated in Table 1.
(a) (b) (c)
Figure 5. Snapshot of the data set (a), input test image (b) and retrieved results (c)
Though the original retreival results from the above Figure 5 shows only the first four to
be relevant, we find that with the help of relevance feedback and genetic algorithms, the
retreival accuracy improves considerably and those results are tabulated in Table 1. Precision
found in the table is defined as number of retrieved images that are relevant divided by total
number of retrieved images from the database. The other parameter recall is defined in the
similar way as the number of retrieved relevant images from the database divided by total
number of relevant images.
Table 1. Recall and Precision Ratio for Different Kinds of Images
Images Recall Ratio (%) Precision Ratio (%)
Flower 85 92
Tiger 72 85
Duck 60 78
Building 74 80
Players 86 90
Bird 78 88
Water 82 86
Elephant 76 92
Sun 80 88
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We have presented two stage retrieval processes in this research work. At the first
stage, we have used the primitive features namely the color and texture which are extracted
from the DCT coefficients of the JPEG compressed image and at the second stage we have
used the genetic algorithm in fine tuning the retrieved results. We find that the results improve a
lot with relevance feedback from the end user when genetic algorithms are deployed in
compressed domain content based image retrieval as compared to the spatial domain content
based image retrieval systems.
7. Future Recommendations
The proposed idea can be extended towards DWT compressed images as used in
JPEG 2000 methodology. Though we have tested against an animal data set, the proposed
approach can be tested against different commercial applications including crime prevention,
security checks, medical diagnosis, intellectual properties, architectural and engineering designs
etc. to lost a few. The field of open questions is still very huge and much interesting work can be
done to get a large and robust feature set in content based image retrieval systems.
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