Nowadays, Content-Based Image Retrieval has received a massive attention in the literature of image information retrieval, and accordingly a broad range of techniques have been proposed. However, these techniques are not free of defects in terms of recognition. In this paper, content based image retrieval has been proposed with a new method of building feature vector to represente an image for the clustertnig, which consiss of 140 elements taken from several feature types as following color historgram, color moments, Gabor filters, GLCM matrix, wavelet transformation, tamura feature, and moment invaraints. Aftering preparing the feature vector, clustering operation named K-Mean is exploited here to give the centroid of each image features. Finally Minkowski-Form Distance and Euclidean distance as a similarity measurement are applied for clustering groups of images having the same charactersitcs, shape and colors. The experiment is run on IMPLIcity database which has 1000 colored images. The evaluation of this proposed algorithm was by selecting random five images as query images, a fruitful result has been gotten as clustering set of images as illustared in the result section of this paper.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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.
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.
The complexity of landscape pattern mining is well stated due to its non-linear spatial image formation and
inhomogeneity of the satellite images. Land Ex tool of the literature work needs several seconds to answer input
image pattern query. The time duration of content based image retrieval depends on input query complexity. This
paper focuses on designing and implementing a training dataset to train NML (Neural network based Machine
Learning) algorithm to reduce the search time to improve the result accuracy. The performance evolution of
proposed NML CBIR (Content Based Image Retrieval) method will be used for comparison of satellite and natural
images by means of increasing speed and accuracy.
Keywords: Spatial Image, Satellite image, NML, CBIR
The incremented desideratum of content based image retrieval system can be found in a number of different domains such as Data Mining, Edification, Medical Imaging, Malefaction Aversion, climate, Remote Sensing and Management of Globe Resources. Google's image search and photo album implements such as image search, Google's Picasa project applications in general gregarious networking environment, the hunt for practical, efficacious image search in the web context. Our application provides the color based image retrieval, utilizing features like dominant color. The color features are obtained through wavelet transformation and color histogram and the amalgamation of these features is robust to scaling and translation of objects in an image. The proposed system has established a promising and more expeditious retrieval method on a input image database containing more general purpose color images. The performance has been analysed by estimating with the subsisting systems in the literature. Dr. Aziz Makandar | Mrs. Rashmi Somshekhar | Miss. Nayan Jadav ""Content Based Image Retrieval"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd24047.pdf
Paper URL: https://www.ijtsrd.com/computer-science/data-miining/24047/content-based-image-retrieval/dr-aziz-makandar
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.
Content based Image Retrieval from Forensic Image DatabasesIJERA Editor
Due to the proliferation of video and image data in digital form, Content based Image Retrieval has become a prominent research topic. In forensic sciences, digital data have been widely used such as criminal images, fingerprints, scene images and so on. Therefore, the arrangement of such large image data becomes a big issue such as how to get an interested image fast. There is a great need for developing an efficient technique for finding the images. In order to find an image, image has to be represented with certain features. Color, texture and shape are three important visual features of an image. Searching for images using color, texture and shape features has attracted much attention. There are many content based image retrieval techniques in the literature. This paper gives the overview of different existing methods used for content based image retrieval and also suggests an efficient image retrieval method for digital image database of criminal photos, using dynamic dominant color, texture and shape features of an image which will give an effective retrieval result.
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.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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.
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.
The complexity of landscape pattern mining is well stated due to its non-linear spatial image formation and
inhomogeneity of the satellite images. Land Ex tool of the literature work needs several seconds to answer input
image pattern query. The time duration of content based image retrieval depends on input query complexity. This
paper focuses on designing and implementing a training dataset to train NML (Neural network based Machine
Learning) algorithm to reduce the search time to improve the result accuracy. The performance evolution of
proposed NML CBIR (Content Based Image Retrieval) method will be used for comparison of satellite and natural
images by means of increasing speed and accuracy.
Keywords: Spatial Image, Satellite image, NML, CBIR
The incremented desideratum of content based image retrieval system can be found in a number of different domains such as Data Mining, Edification, Medical Imaging, Malefaction Aversion, climate, Remote Sensing and Management of Globe Resources. Google's image search and photo album implements such as image search, Google's Picasa project applications in general gregarious networking environment, the hunt for practical, efficacious image search in the web context. Our application provides the color based image retrieval, utilizing features like dominant color. The color features are obtained through wavelet transformation and color histogram and the amalgamation of these features is robust to scaling and translation of objects in an image. The proposed system has established a promising and more expeditious retrieval method on a input image database containing more general purpose color images. The performance has been analysed by estimating with the subsisting systems in the literature. Dr. Aziz Makandar | Mrs. Rashmi Somshekhar | Miss. Nayan Jadav ""Content Based Image Retrieval"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd24047.pdf
Paper URL: https://www.ijtsrd.com/computer-science/data-miining/24047/content-based-image-retrieval/dr-aziz-makandar
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.
Content based Image Retrieval from Forensic Image DatabasesIJERA Editor
Due to the proliferation of video and image data in digital form, Content based Image Retrieval has become a prominent research topic. In forensic sciences, digital data have been widely used such as criminal images, fingerprints, scene images and so on. Therefore, the arrangement of such large image data becomes a big issue such as how to get an interested image fast. There is a great need for developing an efficient technique for finding the images. In order to find an image, image has to be represented with certain features. Color, texture and shape are three important visual features of an image. Searching for images using color, texture and shape features has attracted much attention. There are many content based image retrieval techniques in the literature. This paper gives the overview of different existing methods used for content based image retrieval and also suggests an efficient image retrieval method for digital image database of criminal photos, using dynamic dominant color, texture and shape features of an image which will give an effective retrieval result.
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.
This document summarizes a research paper that proposes a technique for clustering objects in movies using graph mining. It involves segmenting a movie into frames, extracting features of objects in each frame, constructing a graph representing relationships between objects, and applying a graph mining algorithm to cluster objects and determine their behaviors across frames. The algorithm represents each frame as a graph and mines patterns to discover how object clusters change over time. The approach aims to efficiently analyze movie content by modeling the spatial and relational properties between objects in each frame.
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.
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.
This document proposes a novel approach for detecting text in images and using the detected text as keywords to retrieve similar textual images from a database. The approach uses a text detection technique to find text regions in images, eliminates false positives, recognizes the text using OCR, and forms keywords using a neural language model. The detected keywords are then used to index and retrieve similar textual images from two benchmark datasets. Experimental results show the approach effectively retrieves similar textual images by exploiting the dominant text information in the images.
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.
The document proposes developing a content-based image retrieval system using perceptual texture features for biomedical image databases. It performs a literature review of prior work on texture feature extraction and perceptual texture features like coarseness, contrast, directionality and busyness. It then describes computational measures to estimate these perceptual texture features from images and their autocorrelation functions. These include measures of coarseness based on maxima counts, contrast based on autocorrelation function slope, and directionality based on dominant orientations. The proposed system would apply these texture feature extraction and matching techniques to build a knowledge-based expert system for retrieving dental images.
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.
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.
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.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
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 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
This dissertation discusses content-based image retrieval for medical imaging using texture features. The document outlines the background of CBIR and its applications in medical areas. It discusses using Gabor wavelet and gray level co-occurrence matrix (GLCM) texture features to extract features from medical images for retrieval. The methodology section describes extracting contrast, mean, standard deviation, entropy and energy features. Results show precision and recall rates for sample queries of knee, brain and chest images ranging from 79-88%. The conclusion discusses the proposed method's simplicity and speed while achieving average precision of 87.3%. The future scope discusses improving query time and updating the fuzzy rule base.
This document presents a content-based image retrieval semantic model for shaped and unshaped objects. It proposes classifying objects into two categories: shaped objects with a fixed shape like animals and objects, and unshaped objects without a fixed shape like landscapes. For unshaped objects, local regions are classified by frequency of occurrence and semantic concepts are evaluated using color, shape, and regional dissimilarity factors. For shaped objects, semantic concepts are measured using normalized color, edge detection, particle removal, and shape similarity. Several existing content-based image retrieval techniques are also briefly discussed.
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).
Content Based Image Retrieval Using Dominant Color and Texture FeaturesIJMTST Journal
The purpose of this Paper is to describe our research on different feature extraction and matching techniques in designing a Content Based Image Retrieval (CBIR) system. Due to the enormous increase in image database sizes, as well as its vast deployment in various applications, the need for CBIR development arose. Content Based Image Retrieval (CBIR) is the retrieval of images based on features such as color and texture. Image retrieval using color feature cannot provide good solution for accuracy and efficiency. The most important features are Color and texture. In this paper technique used for retrieving the images based on their content namely dominant color, texture and combination of both color and texture. The technique verifies the superiority of image retrieval using multi feature than the single feature.
"The explosive increase and ubiquitous accessibility of visual data on the Web have led to the prosperity of research activity in image search or retrieval. With the ignorance of visual content as a ranking clue, methods with text search techniques for visual retrieval may suffer inconsistency between the text words and visual content. Content based image retrieval CBIR , which makes use of the representation of visual content to identify relevant images, has attracted sustained attention in recent two decades. Such a problem is challenging due to the intention gap and the semantic gap problems. Numerous techniques have been developed for content based image retrieval in the last decade. The purpose of this paper is to categorize and evaluate those algorithms proposed during the period of 2003 to 2016. We conclude with several promising directions for future research. Santosh Kumar Swarnkar | Prof. Avinash Sharma ""Content-Based Image Retrieval: An Assessment"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd21708.pdf
Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/21708/content-based-image-retrieval-an-assessment/santosh-kumar-swarnkar"
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.
This document proposes a novel approach for detecting text in images and using the detected text as keywords to retrieve similar textual images from a database. The approach uses a text detection technique to find text regions in images, eliminates false positives, recognizes the text using OCR, and forms keywords using a neural language model. These keywords are then used to index and retrieve similar textual images based on the detected text. The experimental results on two benchmark datasets show this text-based approach is effective for retrieving textual images.
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.
This document describes a proposed content-based image retrieval system using backpropagation neural networks (BPNN) and k-means clustering. It begins by discussing CBIR techniques and features like color, texture, and shape. It then outlines the proposed system which includes training a BPNN on image features, validating images, and testing by querying and retrieving similar images. Performance is analyzed based on metrics like accuracy, efficiency, and classification rate. Results show the system achieves up to 98% classification accuracy within 5-6 seconds.
This document summarizes a research paper that proposes a technique for clustering objects in movies using graph mining. It involves segmenting a movie into frames, extracting features of objects in each frame, constructing a graph representing relationships between objects, and applying a graph mining algorithm to cluster objects and determine their behaviors across frames. The algorithm represents each frame as a graph and mines patterns to discover how object clusters change over time. The approach aims to efficiently analyze movie content by modeling the spatial and relational properties between objects in each frame.
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.
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.
This document proposes a novel approach for detecting text in images and using the detected text as keywords to retrieve similar textual images from a database. The approach uses a text detection technique to find text regions in images, eliminates false positives, recognizes the text using OCR, and forms keywords using a neural language model. The detected keywords are then used to index and retrieve similar textual images from two benchmark datasets. Experimental results show the approach effectively retrieves similar textual images by exploiting the dominant text information in the images.
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.
The document proposes developing a content-based image retrieval system using perceptual texture features for biomedical image databases. It performs a literature review of prior work on texture feature extraction and perceptual texture features like coarseness, contrast, directionality and busyness. It then describes computational measures to estimate these perceptual texture features from images and their autocorrelation functions. These include measures of coarseness based on maxima counts, contrast based on autocorrelation function slope, and directionality based on dominant orientations. The proposed system would apply these texture feature extraction and matching techniques to build a knowledge-based expert system for retrieving dental images.
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.
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.
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.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
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 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
This dissertation discusses content-based image retrieval for medical imaging using texture features. The document outlines the background of CBIR and its applications in medical areas. It discusses using Gabor wavelet and gray level co-occurrence matrix (GLCM) texture features to extract features from medical images for retrieval. The methodology section describes extracting contrast, mean, standard deviation, entropy and energy features. Results show precision and recall rates for sample queries of knee, brain and chest images ranging from 79-88%. The conclusion discusses the proposed method's simplicity and speed while achieving average precision of 87.3%. The future scope discusses improving query time and updating the fuzzy rule base.
This document presents a content-based image retrieval semantic model for shaped and unshaped objects. It proposes classifying objects into two categories: shaped objects with a fixed shape like animals and objects, and unshaped objects without a fixed shape like landscapes. For unshaped objects, local regions are classified by frequency of occurrence and semantic concepts are evaluated using color, shape, and regional dissimilarity factors. For shaped objects, semantic concepts are measured using normalized color, edge detection, particle removal, and shape similarity. Several existing content-based image retrieval techniques are also briefly discussed.
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).
Content Based Image Retrieval Using Dominant Color and Texture FeaturesIJMTST Journal
The purpose of this Paper is to describe our research on different feature extraction and matching techniques in designing a Content Based Image Retrieval (CBIR) system. Due to the enormous increase in image database sizes, as well as its vast deployment in various applications, the need for CBIR development arose. Content Based Image Retrieval (CBIR) is the retrieval of images based on features such as color and texture. Image retrieval using color feature cannot provide good solution for accuracy and efficiency. The most important features are Color and texture. In this paper technique used for retrieving the images based on their content namely dominant color, texture and combination of both color and texture. The technique verifies the superiority of image retrieval using multi feature than the single feature.
"The explosive increase and ubiquitous accessibility of visual data on the Web have led to the prosperity of research activity in image search or retrieval. With the ignorance of visual content as a ranking clue, methods with text search techniques for visual retrieval may suffer inconsistency between the text words and visual content. Content based image retrieval CBIR , which makes use of the representation of visual content to identify relevant images, has attracted sustained attention in recent two decades. Such a problem is challenging due to the intention gap and the semantic gap problems. Numerous techniques have been developed for content based image retrieval in the last decade. The purpose of this paper is to categorize and evaluate those algorithms proposed during the period of 2003 to 2016. We conclude with several promising directions for future research. Santosh Kumar Swarnkar | Prof. Avinash Sharma ""Content-Based Image Retrieval: An Assessment"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd21708.pdf
Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/21708/content-based-image-retrieval-an-assessment/santosh-kumar-swarnkar"
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.
This document proposes a novel approach for detecting text in images and using the detected text as keywords to retrieve similar textual images from a database. The approach uses a text detection technique to find text regions in images, eliminates false positives, recognizes the text using OCR, and forms keywords using a neural language model. These keywords are then used to index and retrieve similar textual images based on the detected text. The experimental results on two benchmark datasets show this text-based approach is effective for retrieving textual images.
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.
This document describes a proposed content-based image retrieval system using backpropagation neural networks (BPNN) and k-means clustering. It begins by discussing CBIR techniques and features like color, texture, and shape. It then outlines the proposed system which includes training a BPNN on image features, validating images, and testing by querying and retrieving similar images. Performance is analyzed based on metrics like accuracy, efficiency, and classification rate. Results show the system achieves up to 98% classification accuracy within 5-6 seconds.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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.
A hybrid approach for categorizing images based on complex networks and neur...IJECEIAES
There are several methods for categorizing images, the most of which are statistical, geometric, model-based and structural methods. In this paper, a new method for describing images based on complex network models is presented. Each image contains a number of key points that can be identified through standard edge detection algorithms. To understand each image better, we can use these points to create a graph of the image. In order to facilitate the use of graphs, generated graphs are created in the form of a complex network of small-worlds. Complex grid features such as topological and dynamic features can be used to display image-related features. After generating this information, it normalizes them and uses them as suitable features for categorizing images. For this purpose, the generated information is given to the neural network. Based on these features and the use of neural networks, comparisons between new images are performed. The results of the article show that this method has a good performance in identifying similarities and finally categorizing them.
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.
Relevance feedback a novel method to associate user subjectivity to imageIAEME Publication
This document proposes a novel method for combining user subjectivity and relevance feedback in content-based image retrieval systems. It describes a two-step process: 1) Performing image analysis to automatically infer the best combination of models to represent the data of interest to the user, and 2) Capturing the user's high-level query and perceptual subjectivity through dynamically updated weights based on the user's feedback during the retrieval process. The proposed approach aims to reduce the user's effort in composing queries and better capture their information needs over time by continuously learning from user interactions.
Efficient CBIR Using Color Histogram Processingsipij
This document summarizes an article that proposes using color histogram processing to improve the efficiency of content-based image retrieval (CBIR) systems. It describes computing feature vectors for global descriptor attributes to characterize images prior to calculating color histograms, in order to reduce computation time and make the CBIR system more efficient. The performance of using global descriptor attributes and color histograms for image retrieval is evaluated and results are presented. While this approach shows some improved performance over prior methods, the authors conclude that further modifications are still needed to optimize image search capabilities.
This document summarizes a research paper on dynamic hand gesture recognition using content-based image retrieval. It discusses segmenting images into foreground and background regions for hand tracking. Color, texture, and shape features are extracted from segmented regions for content-based image retrieval. A skin detection algorithm is used to identify hand regions based on color properties. A hand gesture recognition system is proposed that acquires images, detects hands using skin detection, and recognizes gestures based on hand motion analysis.
Design and Development of an Algorithm for Image Clustering In Textile Image ...IJCSEA Journal
All textile industries aim to produce competitive materials and the competition enhancement depends mainly on designs and quality of the dresses produced by each industry. Every day, a vast amount of textile images are being generated such as images of shirts, jeans, t-shirts and sarees. A principal driver of innovation is World Wide Web, unleashing publication at the scale of tens and millions of content creators. Images play an important role as a picture is worth thousand words in the field of textile design and marketing. A retrieving of images needs special concepts such as image annotation, context, and image content and image values. This research work aimed at studying the image mining process in detail and analyzes the methods for retrieval. The textile images analyze various methods for clustering the images and developing an algorithm for the same. The retrieval method considered is based on relevance feedback, scalable method, edge histogram and color layout. The image clustering algorithm is designed based on color descriptors and k-means clustering algorithm. A software prototype to prove the proposed algorithm has been developed using net beans integrated development environment and found successful.
Content-based Image Retrieval System for an Image Gallery Search Application IJECEIAES
Content-based image retrieval is a process framework that applies computer vision techniques for searching and managing large image collections more efficiently. With the growth of large digital image collections triggered by rapid advances in electronic storage capacity and computing power, there is a growing need for devices and computer systems to support efficient browsing, searching, and retrieval for image collections. Hence, the aim of this project is to develop a content-based image retrieval system that can be implemented in an image gallery desktop application to allow efficient browsing through three different search modes: retrieval by image query, retrieval by facial recognition, and retrieval by text or tags. In this project, the MPEG-7-like Powered Localized Color and Edge Directivity Descriptor is used to extract the feature vectors of the image database and the facial recognition system is built around the Eigenfaces concept. A graphical user interface with the basic functionality of an image gallery application is also developed to implement the three search modes. Results show that the application is able to retrieve and display images in a collection as thumbnail previews with high retrieval accuracy and medium relevance and the computational requirements for subsequent searches were significantly reduced through the incorporation of text-based image retrieval as one of the search modes. All in all, this study introduces a simple and convenient way of offline image searches on desktop computers and provides a stepping stone to future content-based image retrieval systems built for similar purposes.
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.
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 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.
A Comparative Study of Content Based Image Retrieval Trends and ApproachesCSCJournals
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cnn.pptx Convolutional neural network used for image classication
Content-Based Image Retrieval by Multi-Featrus Extraction and K-Means Clustering
1. International Journal of Electrical, Electronics and Computers (EEC Journal) [Vol-2, Issue-3, May-Jun 2017]
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Content-Based Image Retrieval by Multi-
Features Extraction and K-Means Clustering
Mostafa G. Saeed1
, Fahad Layth Malallah2
, Zaid Ahmed Aljawaryy3
1,2
Department of computer Science, Cihan University / Sulaimaniya, Iraq
3
Faculty of Science and Technology University of Human Development / Sulaimaniya, Kurdistan Region, Iraq
Abstract— Nowadays, Content-Based Image Retrieval
has received a massive attention in the literature of image
information retrieval, and accordingly a broad range of
techniques have been proposed. However, these
techniques are not free of defects in terms of recognition.
In this paper, content based image retrieval has been
proposed with a new method of building feature vector to
represente an image for the clustertnig, which consiss of
140 elements taken from several feature types as
following color historgram, color moments, Gabor filters,
GLCM matrix, wavelet transformation, tamura feature,
and moment invaraints. Aftering preparing the feature
vector, clustering operation named K-Mean is exploited
here to give the centroid of each image features. Finally
Minkowski-Form Distance and Euclidean distance as a
similarity measurement are applied for clustering groups
of images having the same charactersitcs, shape and
colors. The experiment is run on IMPLIcity database
which has 1000 colored images. The evaluation of this
proposed algorithm was by selecting random five images
as query images, a fruitful result has been gotten as
clustering set of images as illustared in the result section
of this paper.
Keywords—Image processing, Pattern Recognition,
Machine learning.
I. INTRODUCTION
A picture worths a thousand words as human beings are
able to tell a story from a picture based on what they see
[1]. Recent years have seen a rapid increase in the size of
digital image collections. Every day, both military and
civilian equipment generate Giga-bytes of images. A huge
amount of information is out there. However, to access of
make use of this information it should be organized to
allow efficient browsing, searching, and retrieval. Image
retrieval has been a very active research area since the
1970's. In various computer vision applications are
widely the process of retrieving desired images from a
large collection on the basis of features that can be
automatically extracted from the images themselves.
These systems called CBIR (Content-Based Image
Retrieval) have received intensive attention in the
literature of image information retrieval since this area
was started years ago, and consequently a broad range of
techniques have been proposed [2]. More and more
images are being readily available to professional and
amateur users because of astonishing advancements in
color imaging technologies. The large numbers of image
collections, available from a variety of sources (digital
camera, digital video, scanner, the internet etc.) have
posed increasing technical challenges to computer
systems to store/transmit and index/manage image data
effectively to make such collections easily available [1]
[2] [3]. In 1991, both Swain and Ballard worked on CBIR
and proposed histogram intersection, an L1 metric, as the
similarity measure for the color histogram. [4]. While in
1994 Niblack et al and his colleagues introduced an L2-
related metric in comparing the histograms [5].
Furthermore, considering that most color histograms are
very sparse and thus sensitive to noise, in 1995 Stricker
and Orengo proposed using the cumulated color
histogram. Their research results demonstrated the
advantages of the proposed approach over the
conventional color histogram approach[6]. In 1995 Both
Stricker and Orengo work on other color features and
proposed using the color moments to overcome the
quantization effects, as in color histogram. most of the
information is concentrated on the low-order moments,
only the first moment (mean), and the second and third
central moments (variance and skewness) [6]. Also In
1995, Smith and Chang worked To facilitate fast search
over large-scale image collections, they proposed color
sets as an approximation to the color histogram. A binary
search tree was constructed to allow a fast search [7] [8].
In 1990 Gotlieb and Kreyszig studied the statistics of the
first constructed co-occurrence matrix originally proposed
in 1973 which explored the gray level spatial dependence
of texture, and experimentally found out that contrast,
inverse deference moment, and entropy had the biggest
discriminatory power [9]. In 1993 Chang and Kuo used a
tree-structured wavelet transform To explore the middle-
band characteristics, to further improve the classification
accuracy [10]. While in 1994, 1996 Smith and Chang
used the statistics (mean and variance) extracted from the
wavelet subbands as the texture representation. This
approach achieved over 90% accuracy on the 112 Brodatz
2. International Journal of Electrical, Electronics and Computers (EEC Journal) [Vol-2, Issue-3, May-Jun 2017]
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texture images [11]. In 1994 Gross and his colleagues
combined, the wavelet transform with other techniques to
achieve better performance. Gross et al. used the wavelet
transform, together with KL expansion and Kohonen
maps, to perform the texture analysis [12]. In 1992, 1994
Thyagarajan et al. and Kundu et al. combined the
wavelet transform with a co-occurrence matrix to take
advantage of both statistics-based and transform-based
texture analyses [13]. In 1994, based on the discrete
version of Green’s theorem, Yang and Albregtsen
proposed a fast method of computing moments in binary
images. Motivated by the fact that most useful invariants
were found by extensive experience and trial-and-error
[14].
In terms of feature extraction, Feature (content) extraction
is the basis of content-based image retrieval. In a broad
sense, features may include both text-based features (key
words, annotations) and visual features (color, texture,
shape, faces). However, since there already exist rich
literature on text-based feature extraction in the DBMS
and information retrieval research communities, we will
confine ourselves to the techniques of visual feature
extraction. Within the visual feature scope, the features
can be further classified as general features and domain
specific features. The former include color, texture, and
shape features while the latter is application-dependent
and may include, for example, human faces and finger
prints.
About the application Applications of CBIR, the CBIR
technology has been used in several applications such as
fingerprint identification, biodiversity information
systems, digital libraries, crime prevention, medicine,
historical research, among others. Some of these
applications are presented in this section [15]. In terms of
Medical Applications the use of CBIR can result in
powerful services that can benefit biomedical information
systems. Three large domains can instantly take
advantage of CBIR techniques: teaching, research, and
diagnostics. From the teaching perspective, searching
tools can be used to find important cases to present to
students. Research also can be enhanced by using services
combining image content information with different kinds
of data. For example, scientists can use mining tools to
discover unusual patterns among textual (e.g., treatments
reports, and patient records) and image content
information. Similarity queries based on image content
descriptors can also help the diagnostic process.
Clinicians usually use similar cases for case-based
reasoning in their clinical decision-making process. In this
sense, while textual data can be used to find images of
interest, visual features can be used to retrieve relevant
information for a clinical case (e.g., comments, related
literature, HTML pages, etc [15].
The objective of this paper is to combine several types of
feature extraction operations and then to build a strong
feacture vector to be input into K-mean classifier. In other
words, the object is doing search based on image to
output set or group of images that have the same
characteristics of the qnuireied image.
This paper is organized as follows. Section II is dedicated
for literature review related to Content-Based Image
Retrieval. In section III, the methodology is proposed. In
Section IV, the experiment and results of thid paper, in
Section V, the conclusion is presented with future work.
II. LITERATURE REVIEW
Content-based image retrieval (CBIR), also known as
query by image content (QBIC) and content-based visual
information retrieval (CBVIR) is the application of
computer vision techniques to the image retrieval
problem, that is, the problem of searching for digital
images in large databases. "Content-based" means that the
search will analyze the actual contents of the image rather
than the metadata such as keywords, tags, and/or
descriptions associated with the image [16]. CBIR is a
new but widely adopted method for finding images from
vast and unannotated image databases. In CBIR images
are indexed on the basis of low-level features, such as
color, texture, and shape that can automatically be derived
from the visual content of the images [15].
2.1. Image Feature Extraction
Feature extraction is the basic of content-based image
retrieval. In a broad sense, features may include both text-
based features (keywords, annotations) and visual features
such as color, texture, shape, faces. CBIR system is
performed based on a comparison of low level features
such as color, shape, and texture etc. extracted from the
images [17]. In general Image Features can be divided in
to three sub-classes; color features, texture features and
shape features.
In terms of color feature, which is one of the most
important features, makes the recognition of images.
Color is a property that depends on the reflection of light
to the processing of that information in the brain. The
color is used every day to tell the difference between
objects, places, and the time of day. Typically, the color
of an image is represented through some color model.
There are various color models to describe color
information. A color model is specified in terms of 3-D
coordinate system and a subspace within that system
where each color is represented by a single point. The
majorty used color space is RGB (red, green, blue), HSV
(hue, saturation, value) and Y,Cb,Cr (luminance and
chrominance), Thus the color content is characterized
by3-channels from some color model. One of the color
3. International Journal of Electrical, Electronics and Computers (EEC Journal) [Vol-2, Issue-3, May-Jun 2017]
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content representations of an image is by using a color
histogram. Statistically, it denotes the joint probability of
the intensities of the three-color channels[17].
RGB colors are called primary colors and are additive, By
varying their combinations, other colors can be obtained
also the representation of the HSV spaces derived from
the RGB space cube, with the main diagonal of the RGB
model, as the vertical axis in HSV [18]. As saturation
varies from 0 to 1, the colors vary from unsaturated (gray)
to saturate (no white component). Hue ranges from 0 to
360 degrees, with variation beginning with red, going
through yellow, green, cyan, blue and magenta and back
to red. Color space form (RGB) and (HSV) are
represented in Figure 1.
Fig.1: The RGB color space and the HSV color space
These color spaces are intuitively corresponding to the
RGB model from which they can be derived through
linear or non-linear transformations. The YCbCr color
space is used in the JPEG and MPEG international coding
standards. In MPEG-7 the YCbCr color space is
demonstrated in Figure 1.
About color histogram feature, a color histogram defined
as a color vector H for a given image as a vector H =
{h[1], h[2], . . . h[i], . . . , h[N]} where i represents a color
in the color histogram, h[i] is the number of pixels in
color i in that image, and N is the number of bins in the
color histogram, i.e., the number of colors in the adopted
color model. In order to compare images of different
sizes, color histograms should be normalized[19].
In terms of color moments, color moments have been
successfully used in many retrieval systems. Color
moments are measures that can be used to differentiate
images based on their features of color depend on
statistical methods. Once calculated, these moments
provide a measurement for color similarity between
images. These values of similarity can then be compared
to the values of images indexed in a database for tasks
like image retrieval. Color moments have been proved to
be efficient and effective in representing color
distributions of images [19]. About Texture Feature,
which has been used to classify and recognize objects and
used in finding similarities between images in multimedia
databases [19].Texture is a very useful characterization
for a wide range of image; It is generally believed that
human visual systems use texture for recognition and
interpretation. In general, color is usually a pixel property
while texture can only be measured from a group of
pixels. A large number of techniques have been proposed
to extract texture features. Based on the domain from
which the texture feature is extracted, they can be broadly
classified into; spatial texture feature and spectral texture
feature extraction methods. For the former approach,
texture features are extracted by computing the pixel
statistics or finding the local pixel structures in original
image domain, whereas the latter transforms an image
into frequency domain and then calculates feature from
the transformed image [19]. A variety of techniques have
been used for measuring texture such as co-occurrence
matrix, Fractals, Gabor filters, variations of wavelet
transform [20].
Gabor Filters, the most common method for texture
feature extraction, Gabor filter has been widely used in
image texture feature extraction. Gabor filter is
specifically designed to sample the entire frequency
domain of an image by characterizing the center
frequency and orientation parameters. The image is
filtered with a bank of Gabor filters or Gabor wavelets of
different preferred spatial frequencies and orientations.
Each wavelet captures energy at a specific frequency and
direction which provide a localized frequency as a feature
vector. Thus, texture features can be extracted from this
group of energy distributions. Given an input image
I(x,y), Gabor wavelet transform convolves I(x,y) with a
set of Gabor filters of different spatial frequencies and
orientations [21].
Wavelet transformation gives information about the
variations in the image at different scales. Discrete
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Wavelet Transform (DWT) represents an image as a sum
of wavelet functions with different locations (shift) and
scales. Any decomposition of an 1D image into wavelet
involves a pair of waveforms: the high frequency
components are corresponding to th detailed parts of an
image while the low frequency components are
corresponding to the smooth parts of an image. DWT for
an image as a 2D signal can be derived from a 1D DWT,
implement 1D DWT to every rows then implement 1D
DWT to every column. Any decomposition of an 2D
image into wavelet involves four sub-band elements
representing LL (Approximation), HL (Vertical Detail),
LH (Horizontal Detail), and HH (Detail), respectively.
The wavelet transform allows for the decomposition of a
signal using a series of elemental functions called
wavelets and scaling, which are created by scaling and
translations of a base function, known as the mother
wavelet [22].
About tamura feature is designed in accordance with
psychological studieson the human perception of texture:
coarseness, contrast, directionality, line-likeness,
regularity, and roughness. They make experiments to test
the significance of the features. They found the first three
features to be very important, which correlate strongly
with the human perception. These three features,
coarseness, contrast, and directionality, are defined as
follows [23].
In terms of shape features is known as an important cue
for human beings to identify and recognize the real-world
objects, whose purpose is to encode simple geometrical
forms such as straight lines in different directions [16].
Shape descriptors can be divided into two main
categories: region based and contour-based methods.
Region-based methods use the whole area of an object for
shape description, while contour-based methods use only
the information present in the contour of an object. In
retrieval applications, a small set of lower order moments
is used to discriminate among different images. The most
common moments are: the geometrical moments, central
moments and the normalized central moments, the
moment invariants, the Zernike moments and the
Legendre moments,(which are based on the theory of
orthogonal polynomials, the complex moments.
2.2. Classification
After feature extraction is done, classifiecation stage is
applied to the prepared feacture vector. In this paper,
retrieval system classifiection is applied as a
classifiection, which combines these feature vectors and
calculates the similarity between the combined feature
vectors of the query image, and retrieves a given number
of the most similar target images [24]. Different similarity
or so called distance measures will be affected
significantly on the retrieval performances of an image
retrieval system [25]. On the most popular used method of
distance measurement is Standardized Euclidean distance,
which is calculated on standardized data, in this case
standardized by the standard deviations and it can be
calculated as in equation (1).
(1)
In terms of clustering alogirthm, K-Means Clustering
[26] is commonely used algorithm, which is a partitioning
based clustering. K-Means clustering is used to group n
objects into k clusters to guarantee the resemblance
among objects in the same cluster and the dissimilarity
among samples in different cluster.
III. METHODOLOGY
The methodology consists of three steps, which are image
collecting a database, feature extraction, and k-Mean
clustering algorithm with similarity measurement, as
shown in Figure 2.
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Fig.2: Block diagram for image retrieval system
3.1. Database and data pre-processing
Fig.3: Image categorization
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In this paper, the dataset has been used from database
named SIMPLIcity as referenced in [27], which contains
1000 coloured images. The database was downloaded
from the website [27]. Sizes of the images in this database
are either 256 × 384 or 384 × 256 pixels. The images in
this database are several types and various kinds as shown
in Fgiure 3, in order to generalize this study in the
processing and to get more accurate results. For instance,
this database has various group categories of image
around people, animals, different colors, landscape
groups, structure groups, flower groups and shape group
images.
3.2. Feature Extraction
The feature extraction process aims to describe each
image in the database in terms of low level features.
Feature extraction is a fundamental component in a CBIR
system. For this module, actually, occur in both pre-
processing stage and the time when users do request to
system with an image query. The objective of feature
extraction is to automatically determine a set of features
to describe each image. In this step, the features of images
data are extracted from images. These low level features,
known as descriptors, are used to provide similarity
measures between different images. Descriptors are
typically smaller in size compared to the original image.
The feature extraction flowchart is illustrated in Figure 2.
It is worth to mention that each image is represented with
a feature vector to be input to the K-mean clustering. As
overall, the number of elements of each image feacture
vector is 140 features generated as a combination of the
following features as follows: color historgram, color
moments, Gabor filters, GLCM matrix, wavelet
transformation, tamura feature, and moment invaraints.
As detailed, color historgram which is color histogram
feature values are obtained after converting to HSV, color
moments which are color moments feature values are
obtained after reading an image and dividing it to four
segments then calculate the color variance of each
segment then combine the segment variances into one
single variance, Gabor filters which are values of this
filter taken to RBG image after converting to gray scale
image, GLCM matrix which is RGB image converted to
2D gray scale format, then taken their means and
variances of all the parameters, wavelet transformation
which are values found by reading the RGB image and
resizing it to the size of MxN for M=N=256 where M is
the number of rows, N is the number of columns, without
information loss, after that convert it to 2D gray-scale
format, then decompose the image into sub-images, then
extracting the feature vector, tamura feature, and moment
invaraints which are obtained by converting the RGB
image to grayscale, then applying Haar filter on it.
3.3. Image Clustering
In clustering operation by K-means, the 1000 images in
the database are grouped into 100 separated groups. Each
group represents a center in the clustering algorithm. In
other words, similar set of pictures are put together in
every single group. The center of a group has an average
of the features of the images belong to that group, for
instance, cluster #21 containing images #701, #707, #714,
and #717 which are similar to the query image having
similar features as wil be illustrated i the result section.
The purpose of image clustering is to decrease the number
of image (or features) vectors compared with the query
image. The query is compared to the centroids only, the
best clusters are then selected and the images that belong
to that cluster are retrieved. It iwork to explain K- Means
Clustering optimizes only intra cluster similarity. The
steps for k-means algorithm are as follows: initialize
number of clusters, then, randomly choose centroid from
database, after that compute the Euclidean distance
between data points and cluster centroid using equation
(2).
(2)
And then the clusters are created based on minimum
distance after that, update the cluster centroid by
computing the mean for clusters. Finally, the procedure is
continued until the mean values are same for several
consecutive iterations [28].
IV. RESULT AND DISCUSSION
To test the proposed algorithm of image retrival, five
input images have been used as a query images, and these
five images each of which will show a set of similar
images as a group.In figure 4, qiurey image which is
described as face, has sequence number in the database as
#7, the result of its k-menas clustering is 13 images that
have been clustered into one group as shown in Figure 4.
These 13 images have the following sequence number in
the database as: 9,10,24,32,39,40,41,43,57,75, 81, 95 and
933.
For the second test image, which is in in figure 5, qiurey
image which is described as flower, has sequence number
in the database as #600, the result of its k-menas
clustering is 14 images that have been clustered into one
group as shown in Figure 5. These 14 images have the
following sequence number in the database as: 600 ,605,
609, 614, 615, 623, 628, 631, 633, 641, 644, 647,
653,666 and 679.
For the third test image, which is in in figure 6, qiurey
image which is described as mountain, has sequence
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number in the database as #844, the result of its k-menas
clustering is 4 images that have been clustered into one
group as shown in Figure 6. These 4 images have the
following sequence number in the database as: 804, 881
,892 and 943.
For the forth test image, which is in in figure 7, qiurey
image which is described as dinosaur, has sequence
number in the database as #488, the result of its k-menas
clustering is 9 images that have been clustered into one
group as shown in Figure 7. These 9 images have the
following sequence number in the database as: 412, 429,
446, 457,470, 473 482,490 and 495.
Finally the fifth test image, which is in in Figure 8, qiurey
image is described as Bus that has sequence number in the
database as #332, the result of its k-menas clustering is 5
images that have been clustered into one group as shown
in Figure 8. These 5 images have the following sequence
number in the database as: 309,330, 334, 357, 374 and
382.
Query Image
Target (result) images
as one cluster for faces
Fig.4: Clustering Output for query image as face #7.
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Query Image
Target (result) images
as one cluster for
flowers
Fig.5: Clustering Output for query image as flower #600.
Query Image
Target (result) images
as one cluster for
mountains
Fig.6: Clustering Output for query image as mountains #844.
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Query Image
Target (result) images
as one cluster for
dinosaur
Fig.7: Clustering Output for Query Image as Dinosaur #488.
Query Image
Target (result)
images as one cluster
for Bus
Fig.8: Clustering Output for Query Image as Bus #332.
The previous five query images have been shosen
randomly to do the test, as it is cleqar in each cluster
groups tere are images as the same as the quiry image.
However, the databasr is challenging and not trivial tsk to
achieve 100% as fuccessful accuracy. Therefore this
proposed moetgos promis with a fuitful result in terms of
clustering images.
V. CONCLUSION
In this paper contnet based image retrievl (CBIR) has
been ahcievd by using a proposed collection of features
and clustered by using k-means clustering algorithm after
that an Euclidean distance has been used for the distance
measurement to make the decision of the cultering group.
The new idea in this paper is how to collect and build the
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feature vector to represent the image. In this paper, 140
features elements have been utilized as a combination of
several feature types as follows: color historgram, color
moments, Gabor filters, GLCM matrix, wavelet
transformation, tamura feature, and moment invaraints.
Result of this research has a promising outcome as the
experiment conducted on IMPLIcity database which has
1000 images. In the testing, five randomly selected
images from this database have been used as query
images. The result noticed was clustering several images
as a group of each query image.
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