The document proposes an approach combining automatic relevance feedback and particle swarm optimization for image retrieval. It constructs a visual feature database from image features like color moments and Gabor filters. For a query image, it retrieves similar images and generates automatic relevance feedback by labeling images as relevant or irrelevant. It then uses particle swarm optimization to re-weight features and retrieve more relevant images over multiple iterations, splitting the swarm in later iterations. An experiment on Corel images over 5 classes showed the approach could effectively retrieve relevant images through this meta-heuristic process without human interaction.
Improving the Accuracy of Object Based Supervised Image Classification using ...CSCJournals
A lot of research has been undertaken and is being carried out for developing an accurate classifier for extraction of objects with varying success rates. Most of the commonly used advanced classifiers are based on neural network or support vector machines, which uses radial basis functions, for defining the boundaries of the classes. The drawback of such classifiers is that the boundaries of the classes as taken according to radial basis function which are spherical while the same is not true for majority of the real data. The boundaries of the classes vary in shape, thus leading to poor accuracy. This paper deals with use of new basis functions, called cloud basis functions (CBFs) neural network which uses a different feature weighting, derived to emphasize features relevant to class discrimination, for improving classification accuracy. Multi layer feed forward and radial basis functions (RBFs) neural network are also implemented for accuracy comparison sake. It is found that the CBFs NN has demonstrated superior performance compared to other activation functions and it gives approximately 3% more accuracy.
An Impact on Content Based Image Retrival A Perspective Viewijtsrd
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. We conclude with several promising directions for future research. Shivanshu Jaiswal | Dr. Avinash Sharma ""An Impact on Content Based Image Retrival: A Perspective View"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020, URL: https://www.ijtsrd.com/papers/ijtsrd29969.pdf
Paper Url : https://www.ijtsrd.com/engineering/computer-engineering/29969/an-impact-on-content-based-image-retrival-a-perspective-view/shivanshu-jaiswal
SIGNIFICANCE OF DIMENSIONALITY REDUCTION IN IMAGE PROCESSING sipij
The aim of this paper is to present a comparative study of two linear dimension reduction methods namely
PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis). The main idea of PCA is to
transform the high dimensional input space onto the feature space where the maximal variance is
displayed. The feature selection in traditional LDA is obtained by maximizing the difference between
classes and minimizing the distance within classes. PCA finds the axes with maximum variance for the
whole data set where LDA tries to find the axes for best class seperability. The neural network is trained
about the reduced feature set (using PCA or LDA) of images in the database for fast searching of images
from the database using back propagation algorithm. The proposed method is experimented over a general
image database using Matlab. The performance of these systems has been evaluated by Precision and
Recall measures. Experimental results show that PCA gives the better performance in terms of higher
precision and recall values with lesser computational complexity than LDA
Faro Visual Attention For Implicit Relevance Feedback In A Content Based Imag...Kalle
In this paper we propose an implicit relevance feedback method with the aim to improve the performance of known Content Based Image Retrieval (CBIR) systems by re-ranking the retrieved images according to users’ eye gaze data. This represents a new mechanism for implicit relevance feedback, in fact usually the sources taken into account for image retrieval are based on the natural behavior of the user in his/her environment estimated by analyzing mouse and keyboard interactions. In detail, after the retrieval of the images by querying CBIRs with a keyword, our system computes the most salient regions (where users look with a greater interest) of the retrieved images by gathering data from an unobtrusive eye tracker, such as Tobii T60. According to the features, in terms of color, texture, of these relevant regions our system is able to re-rank the images, initially, retrieved by the CBIR. Performance evaluation, carried out on a set of 30 users by using Google Images and “pyramid” like keyword, shows that about the 87% of the users is more satisfied of the output images when the re-raking is applied.
Improving the Accuracy of Object Based Supervised Image Classification using ...CSCJournals
A lot of research has been undertaken and is being carried out for developing an accurate classifier for extraction of objects with varying success rates. Most of the commonly used advanced classifiers are based on neural network or support vector machines, which uses radial basis functions, for defining the boundaries of the classes. The drawback of such classifiers is that the boundaries of the classes as taken according to radial basis function which are spherical while the same is not true for majority of the real data. The boundaries of the classes vary in shape, thus leading to poor accuracy. This paper deals with use of new basis functions, called cloud basis functions (CBFs) neural network which uses a different feature weighting, derived to emphasize features relevant to class discrimination, for improving classification accuracy. Multi layer feed forward and radial basis functions (RBFs) neural network are also implemented for accuracy comparison sake. It is found that the CBFs NN has demonstrated superior performance compared to other activation functions and it gives approximately 3% more accuracy.
An Impact on Content Based Image Retrival A Perspective Viewijtsrd
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. We conclude with several promising directions for future research. Shivanshu Jaiswal | Dr. Avinash Sharma ""An Impact on Content Based Image Retrival: A Perspective View"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020, URL: https://www.ijtsrd.com/papers/ijtsrd29969.pdf
Paper Url : https://www.ijtsrd.com/engineering/computer-engineering/29969/an-impact-on-content-based-image-retrival-a-perspective-view/shivanshu-jaiswal
SIGNIFICANCE OF DIMENSIONALITY REDUCTION IN IMAGE PROCESSING sipij
The aim of this paper is to present a comparative study of two linear dimension reduction methods namely
PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis). The main idea of PCA is to
transform the high dimensional input space onto the feature space where the maximal variance is
displayed. The feature selection in traditional LDA is obtained by maximizing the difference between
classes and minimizing the distance within classes. PCA finds the axes with maximum variance for the
whole data set where LDA tries to find the axes for best class seperability. The neural network is trained
about the reduced feature set (using PCA or LDA) of images in the database for fast searching of images
from the database using back propagation algorithm. The proposed method is experimented over a general
image database using Matlab. The performance of these systems has been evaluated by Precision and
Recall measures. Experimental results show that PCA gives the better performance in terms of higher
precision and recall values with lesser computational complexity than LDA
Faro Visual Attention For Implicit Relevance Feedback In A Content Based Imag...Kalle
In this paper we propose an implicit relevance feedback method with the aim to improve the performance of known Content Based Image Retrieval (CBIR) systems by re-ranking the retrieved images according to users’ eye gaze data. This represents a new mechanism for implicit relevance feedback, in fact usually the sources taken into account for image retrieval are based on the natural behavior of the user in his/her environment estimated by analyzing mouse and keyboard interactions. In detail, after the retrieval of the images by querying CBIRs with a keyword, our system computes the most salient regions (where users look with a greater interest) of the retrieved images by gathering data from an unobtrusive eye tracker, such as Tobii T60. According to the features, in terms of color, texture, of these relevant regions our system is able to re-rank the images, initially, retrieved by the CBIR. Performance evaluation, carried out on a set of 30 users by using Google Images and “pyramid” like keyword, shows that about the 87% of the users is more satisfied of the output images when the re-raking is applied.
Tag based image retrieval (tbir) using automatic image annotationeSAT Journals
Abstract In recent days, several social networking sites are more popular with digitized images. It comprises the major portion of the databases which makes the search engines to face difficulty in searching. We present a proficient image retrieval technique, which achieves eminent retrieval efficiency. Most of the images are annotated manually, thus the visual content and tags may be mismatched. This leads to poor performance in Tag Based Image Retrieval (TBIR). Automatic Image Annotation (AIA) analyzes the missing and noisy tags and over-refines it to increase the performance of TBIR. AIA can be achieved using the Tag Completion algorithm. The images retrieved from the TBIR are ranked based on the relevancy of the tags and visual content of the images. The relevancy can be evaluated using Content Based Image Retrieval (CBIR) technique. Based on the ranks, the images are indexed in the Tag matrix. Thus the images that match the search query can be retrieved in an optimal way. Keywords: Image Retrieval, Automatic Image Annotation, Tag Based Image Retrieval (TBIR), Tag Completion Algorithm, Content Based Image Retrieval (CBIR), Tag Matrix
Novel Hybrid Approach to Visual Concept Detection Using Image AnnotationCSCJournals
Millions of images are being uploaded on the internet without proper description (tags) about these images. Image retrieval based on image tagging approach is much faster than Content Based Image Retrieval (CBIR) approach but requires an entire image collection to be manually annotated with proper tags. This requires a lot of human efforts and time, and hence not feasible for huge image collections. An efficient method is necessary for automatically tagging such a vast collection of images. We propose a novel image tagging method, which automatically tags any image with its concept. Our unique approach to solve this problem involves manual tagging of small exemplar image set and low-level feature extraction of all the images, hence called a hybrid approach. This approach can be used to tag a large image dataset from manually tagged small image dataset. The experiments are performed on Wang's Corel Dataset. In the comparative study, it is found that, the proposed concept detection system based on this novel tagging approach has much less time complexity of classification step, and results in significant improvement in accuracy as compared to the other tagging approaches found in the literature. This approach may be used as faster alternative to the typical Content Based Image Retrieval (CBIR) approach for domain specific applications.
MULTI-LEVEL FEATURE FUSION BASED TRANSFER LEARNING FOR PERSON RE-IDENTIFICATIONgerogepatton
Most of the currently known methods treat person re-identification task as classification problem and used commonly neural networks. However, these methods used only high-level convolutional feature or to express the feature representation of pedestrians. Moreover, the current data sets for person reidentification is relatively small. Under the limitation of the number of training set, deep convolutional networks are difficult to train adequately. Therefore, it is very worthwhile to introduce auxiliary data sets help training. In order to solve this problem, this paper propose a novel method of deep transfer learning, and combines the comparison model with the classification model and multi-level fusion of the
convolution features on the basis of transfer learning. In a multi-layers convolutional network, the characteristics of each layer of network are the dimensionality reduction of the previous layer of results, but the information of multi-level features is not only inclusive, but also has certain complementarity. We can using the information gap of different layers of convolutional neural networks to extract a better feature expression. Finally, the algorithm proposed in this paper is fully tested on four data sets (VIPeR, CUHK01, GRID and PRID450S). The obtained re-identification results prove the effectiveness of the algorithm.
A Survey On: Content Based Image Retrieval Systems Using Clustering Technique...IJMIT JOURNAL
Content-based image retrieval (CBIR) is a new but widely adopted method for finding images from vast and unannotated image databases. As the network and development of multimedia technologies are becoming more popular, users are not satisfied with the traditional information retrieval techniques. So now a days the content based image retrieval (CBIR) are becoming a source of exact and fast retrieval. In recent years, a variety of techniques have been developed to improve the performance of CBIR. Data clustering is an unsupervised method for extraction hidden pattern from huge data sets. With large data sets, there is possibility of high dimensionality. Having both accuracy and efficiency for high dimensional data sets with enormous number of samples is a challenging arena. In this paper the clustering techniques are discussed and analyzed. Also, we propose a method HDK that uses more than one clustering technique to improve the performance of CBIR. This method makes use of hierarchical and divide and conquer K Means clustering technique with equivalency and compatible relation concepts to improve the performance of the K-Means for using in high dimensional datasets. It also introduced the feature like color, texture and shape for accurate and effective retrieval system.
The efficiency and quality of a feature descriptor are critical to the user experience of many computer vision applications. However, the existing descriptors are either too computationally expensive to achieve real-time performance, or not sufficiently distinctive to identify correct matches from a large database with various transformations. In this paper, we propose a highly efficient and distinctive binary descriptor, called local difference binary (LDB). LDB directly computes a binary string for an image patch using simple intensity and gradient difference tests on pair wise grid cells within the patch. A multiple-gridding strategy and a salient bit-selection method are applied to capture the distinct patterns of the patch at different spatial granularities. Experimental results demonstrate that compared to the existing state-of-the-art binary descriptors, primarily designed for speed, LDB has similar construction efficiency, while achieving a greater accuracy and faster speed for mobile object recognition and tracking tasks.
ADOPTING AND IMPLEMENTATION OF SELF ORGANIZING FEATURE MAP FOR IMAGE FUSIONijistjournal
A different image fusion algorithm based on self organizing feature map is proposed in this paper, aiming to produce quality images. Image Fusion is to integrate complementary and redundant information from multiple images of the same scene to create a single composite image that contains all the important features of the original images. The resulting fused image will thus be more suitable for human and machine perception or for further image processing tasks. The existing fusion techniques based on either direct operation on pixels or segments fail to produce fused images of the required quality and are mostly application based. The existing segmentation algorithms become complicated and time consuming when multiple images are to be fused. A new method of segmenting and fusion of gray scale images adopting Self organizing Feature Maps(SOM) is proposed in this paper. The Self Organizing Feature Maps is adopted to produce multiple slices of the source and reference images based on various combination of gray scale and can dynamically fused depending on the application. The proposed technique is adopted and analyzed for fusion of multiple images. The technique is robust in the sense that there will be no loss in information due to the property of Self Organizing Feature Maps; noise removal in the source images done during processing stage and fusion of multiple images is dynamically done to get the desired results. Experimental results demonstrate that, for the quality multifocus image fusion, the proposed method performs better than some popular image fusion methods in both subjective and objective qualities.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal,
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.
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.
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
Due to recent development in technology, there is an increase in the usage of digital cameras, smartphones, and Internet. The shared and stored multimedia data are growing, and to search or to retrieve a relevant image from an archive is a challenging research problem. The fundamental need of any image retrieval model is to search and arrange the images that are in a visual semantic re- lationship with the query given by the user Content Based Image Retrieval Project.
http://takeoffprojects.com/content-based-image-retrieval-project
We are providing you with some of the greatest ideas for building Final Year projects with proper guidance and assistance
An Enhance Image Retrieval of User Interest Using Query Specific Approach and...IJSRD
In recent years, image retrieval process has increased artistically. An image retrieval system is a process for searching and retrieving images from large amount of the image dataset. Color, texture and edge have been the primitive low level image descriptors in content based image retrieval systems. In this paper we discover a system which splits the search process into two stages. In the query specify approach the feature descriptors of a query image we re-extracted and then used to check the similarity between the query image and those images which is in database. In the evolution stage, the most relevant images where retrieved by using the Interactive genetic algorithm. IGA help the users to retrieve the images that are most relevant to the users’ need and SVM will rank the image as their title and as par time of search. So that user can get search image as par their requirements.
Tag based image retrieval (tbir) using automatic image annotationeSAT Journals
Abstract In recent days, several social networking sites are more popular with digitized images. It comprises the major portion of the databases which makes the search engines to face difficulty in searching. We present a proficient image retrieval technique, which achieves eminent retrieval efficiency. Most of the images are annotated manually, thus the visual content and tags may be mismatched. This leads to poor performance in Tag Based Image Retrieval (TBIR). Automatic Image Annotation (AIA) analyzes the missing and noisy tags and over-refines it to increase the performance of TBIR. AIA can be achieved using the Tag Completion algorithm. The images retrieved from the TBIR are ranked based on the relevancy of the tags and visual content of the images. The relevancy can be evaluated using Content Based Image Retrieval (CBIR) technique. Based on the ranks, the images are indexed in the Tag matrix. Thus the images that match the search query can be retrieved in an optimal way. Keywords: Image Retrieval, Automatic Image Annotation, Tag Based Image Retrieval (TBIR), Tag Completion Algorithm, Content Based Image Retrieval (CBIR), Tag Matrix
Novel Hybrid Approach to Visual Concept Detection Using Image AnnotationCSCJournals
Millions of images are being uploaded on the internet without proper description (tags) about these images. Image retrieval based on image tagging approach is much faster than Content Based Image Retrieval (CBIR) approach but requires an entire image collection to be manually annotated with proper tags. This requires a lot of human efforts and time, and hence not feasible for huge image collections. An efficient method is necessary for automatically tagging such a vast collection of images. We propose a novel image tagging method, which automatically tags any image with its concept. Our unique approach to solve this problem involves manual tagging of small exemplar image set and low-level feature extraction of all the images, hence called a hybrid approach. This approach can be used to tag a large image dataset from manually tagged small image dataset. The experiments are performed on Wang's Corel Dataset. In the comparative study, it is found that, the proposed concept detection system based on this novel tagging approach has much less time complexity of classification step, and results in significant improvement in accuracy as compared to the other tagging approaches found in the literature. This approach may be used as faster alternative to the typical Content Based Image Retrieval (CBIR) approach for domain specific applications.
MULTI-LEVEL FEATURE FUSION BASED TRANSFER LEARNING FOR PERSON RE-IDENTIFICATIONgerogepatton
Most of the currently known methods treat person re-identification task as classification problem and used commonly neural networks. However, these methods used only high-level convolutional feature or to express the feature representation of pedestrians. Moreover, the current data sets for person reidentification is relatively small. Under the limitation of the number of training set, deep convolutional networks are difficult to train adequately. Therefore, it is very worthwhile to introduce auxiliary data sets help training. In order to solve this problem, this paper propose a novel method of deep transfer learning, and combines the comparison model with the classification model and multi-level fusion of the
convolution features on the basis of transfer learning. In a multi-layers convolutional network, the characteristics of each layer of network are the dimensionality reduction of the previous layer of results, but the information of multi-level features is not only inclusive, but also has certain complementarity. We can using the information gap of different layers of convolutional neural networks to extract a better feature expression. Finally, the algorithm proposed in this paper is fully tested on four data sets (VIPeR, CUHK01, GRID and PRID450S). The obtained re-identification results prove the effectiveness of the algorithm.
A Survey On: Content Based Image Retrieval Systems Using Clustering Technique...IJMIT JOURNAL
Content-based image retrieval (CBIR) is a new but widely adopted method for finding images from vast and unannotated image databases. As the network and development of multimedia technologies are becoming more popular, users are not satisfied with the traditional information retrieval techniques. So now a days the content based image retrieval (CBIR) are becoming a source of exact and fast retrieval. In recent years, a variety of techniques have been developed to improve the performance of CBIR. Data clustering is an unsupervised method for extraction hidden pattern from huge data sets. With large data sets, there is possibility of high dimensionality. Having both accuracy and efficiency for high dimensional data sets with enormous number of samples is a challenging arena. In this paper the clustering techniques are discussed and analyzed. Also, we propose a method HDK that uses more than one clustering technique to improve the performance of CBIR. This method makes use of hierarchical and divide and conquer K Means clustering technique with equivalency and compatible relation concepts to improve the performance of the K-Means for using in high dimensional datasets. It also introduced the feature like color, texture and shape for accurate and effective retrieval system.
The efficiency and quality of a feature descriptor are critical to the user experience of many computer vision applications. However, the existing descriptors are either too computationally expensive to achieve real-time performance, or not sufficiently distinctive to identify correct matches from a large database with various transformations. In this paper, we propose a highly efficient and distinctive binary descriptor, called local difference binary (LDB). LDB directly computes a binary string for an image patch using simple intensity and gradient difference tests on pair wise grid cells within the patch. A multiple-gridding strategy and a salient bit-selection method are applied to capture the distinct patterns of the patch at different spatial granularities. Experimental results demonstrate that compared to the existing state-of-the-art binary descriptors, primarily designed for speed, LDB has similar construction efficiency, while achieving a greater accuracy and faster speed for mobile object recognition and tracking tasks.
ADOPTING AND IMPLEMENTATION OF SELF ORGANIZING FEATURE MAP FOR IMAGE FUSIONijistjournal
A different image fusion algorithm based on self organizing feature map is proposed in this paper, aiming to produce quality images. Image Fusion is to integrate complementary and redundant information from multiple images of the same scene to create a single composite image that contains all the important features of the original images. The resulting fused image will thus be more suitable for human and machine perception or for further image processing tasks. The existing fusion techniques based on either direct operation on pixels or segments fail to produce fused images of the required quality and are mostly application based. The existing segmentation algorithms become complicated and time consuming when multiple images are to be fused. A new method of segmenting and fusion of gray scale images adopting Self organizing Feature Maps(SOM) is proposed in this paper. The Self Organizing Feature Maps is adopted to produce multiple slices of the source and reference images based on various combination of gray scale and can dynamically fused depending on the application. The proposed technique is adopted and analyzed for fusion of multiple images. The technique is robust in the sense that there will be no loss in information due to the property of Self Organizing Feature Maps; noise removal in the source images done during processing stage and fusion of multiple images is dynamically done to get the desired results. Experimental results demonstrate that, for the quality multifocus image fusion, the proposed method performs better than some popular image fusion methods in both subjective and objective qualities.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal,
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.
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.
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
Due to recent development in technology, there is an increase in the usage of digital cameras, smartphones, and Internet. The shared and stored multimedia data are growing, and to search or to retrieve a relevant image from an archive is a challenging research problem. The fundamental need of any image retrieval model is to search and arrange the images that are in a visual semantic re- lationship with the query given by the user Content Based Image Retrieval Project.
http://takeoffprojects.com/content-based-image-retrieval-project
We are providing you with some of the greatest ideas for building Final Year projects with proper guidance and assistance
An Enhance Image Retrieval of User Interest Using Query Specific Approach and...IJSRD
In recent years, image retrieval process has increased artistically. An image retrieval system is a process for searching and retrieving images from large amount of the image dataset. Color, texture and edge have been the primitive low level image descriptors in content based image retrieval systems. In this paper we discover a system which splits the search process into two stages. In the query specify approach the feature descriptors of a query image we re-extracted and then used to check the similarity between the query image and those images which is in database. In the evolution stage, the most relevant images where retrieved by using the Interactive genetic algorithm. IGA help the users to retrieve the images that are most relevant to the users’ need and SVM will rank the image as their title and as par time of search. So that user can get search image as par their requirements.
A NOVEL WEB IMAGE RE-RANKING APPROACH BASED ON QUERY SPECIFIC SEMANTIC SIGNAT...Journal For Research
Image re-ranking, is an effective way to improve the results of web-based image search. Given a query keyword, a pool of images are initailly retrieved primarily based on textual data, the remaining images are re-ranked based on their visual similarities with the query image corresponding to the user input. A major challenge is that the similarities of visual features don't well correlate with images’ semantic meanings that interpret users’ search intention. Recently people proposed to match pictures in a semantic space that used attributes or reference categories closely associated with the semantic meanings of images as basis. Even though, learning a universal visual semantic space to characterize extremely diverse images from the internet is troublesome and inefficient. In this thesis, we propose a completely distinctive image re-ranking framework that learns completely different semantic spaces for numerous query keywords automatically at the on-line stage. The visual features of images are projected into their corresponding semantic spaces to induce semantic signatures. At the online stage, images are re-ranked by scrutiny their semantic signatures obtained from the semantic spaces such that by the query keyword. The proposed query-specific semantic signatures considerably improve both the accuracy and efficiency of image re-ranking.
Research Inventy : International Journal of Engineering and Scienceinventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
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
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 hugeamount 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.
Tag based image retrieval (tbir) using automatic image annotationeSAT Publishing House
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
WEB IMAGE RETRIEVAL USING CLUSTERING APPROACHEScscpconf
Image retrieval system is an active area to propose a new approach to retrieve images from the
large image database. In this concerned, we proposed an algorithm to represent images using
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META-HEURISTICS BASED ARF OPTIMIZATION FOR IMAGE RETRIEVAL
1. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, No.2, April 2012
DOI : 10.5121/ijcseit.2012.2215 177
META-HEURISTICS BASED ARF OPTIMIZATION
FOR IMAGE RETRIEVAL
Darsana B1
and Dr. Rajan Chattamvelli2
1
The Oxford College of Engineering, Bangalore, India.
darsana.b@live.com
2
Periyar Maniammai University,Thanjavur, India.
cvrajan@gmail.com
ABSTRACT
The proposed approach avoids the semantic gap in image retrieval by combining automatic relevance
feedback and a modified stochastic algorithm. A visual feature database is constructed from the image
database, using combined feature vector. Very few fast-computable features are included in this step. The
user selects the query image, and based on that, the system ranks the whole dataset. The nearest images are
retrieved and the first automatic relevance feedback is generated. The combined similarity of textual and
visual feature space using Latent Semantic Indexing is evaluated and the images are labelled as relevant or
irrelevant. The feedback drives a feature re-weighting process and is routed to the particle swarm
optimizer. Instead of classical swarm update approach, the swarm is split, for each swarm to perform the
search in parallel, thereby increasing the performance of the system. It provides a powerful optimization
tool and an effective space exploration mechanism. The proposed approach aims to achieve the following
goals without any human interaction - to cluster relevant images using meta-heuristics and to dynamically
modify the feature space by feeding automatic relevance feedback.
KEYWORDS
Content based Image Retrieval, Latent Semantic Indexing, Meta-heuristics, Semantic gap.
1. INTRODUCTION
Image retrieval plays a vital role in Image Archives, Domain Specific Collections, Enterprise
Collections, Personal Collections, and the Web. An image retrieval system is a software system
for browsing, searching and retrieving images from a large database of digital images. Retrieval
process includes, submitting a query, and extracting images that best matches the user request.
The query may be text-based or image-based. Image retrieval is broadly classified into two
divisions - Image meta search and Content Based Image Retrieval (CBIR).
Image meta search deals with search of images based on associated metadata such as keywords,
text, etc. Inherent problem is that annotating thousands of images is a questionable process, and
the search results are highly dependent on the subjectivity of human perception. Based on the
human perception, the search tags may vary, which brings up evident differences in the retrieved
results.
The paramount challenge of semantic gap is bridged by Content-based Image Retrieval [1], which
is one of the hot spots in the field of image retrieval in recent years. CBIR filters out images
2. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, No.2, April 2012
178
based on similarities in their contents to a user-supplied query image. In other words, it extracts
images’ visual information by analysing the inherent data in images. However, the algorithm of a
single visual image feature represents only a partial property of the image [2], so search results
have not been very good. In the extraction of combined features, traditional methods mostly
extract manifold features from the original image. This has the disadvantage that the feature
vector is too big and computational complexity is too high, especially for large databases.
The performance of these image-centric retrieval systems is not satisfactory primarily due to the
mismatch between the user’s implied concept and the low-level visual features. In order to narrow
this gap, relevance feedback was introduced as an interactive tool in CBlR [3].
Although relevance feedback can significantly improve the retrieval performance in CBlR
systems, its applicability still suffers from two inherent problems. First, user interaction for
providing feedback is time consuming and tiring. Secondly, previous feedback results are
typically not retained in the system.
This paper is organised as follows - In Section 2 we briefly summarise the related work on image
retrieval, relevance feedback and meta-heuristics. The proposed approach is described in Section
3. In Section 4, we present experimental setup, results and discussions. Finally, Section 5 draws
the conclusions and identifies future research directions.
2. RELATED WORK
2.1. Content Based Image Retrieval
In recent years, CBIR systems have gained much popularity. Huge number of images makes it
difficult to locate the image searched for, especially when the search is based on the esthetic value
of the image. Content Based Image Retrieval (CBIR) uses a query image to retrieve all matching
images within a specified threshold. Major area of focus in this, is the semantic gap, i.e.,
understanding the subjective meaning of the visual query. The less the semantic gap is, the results
are more accurate. Most of the research has concentrated on feature extraction of an image, e.g.,
QBIC [4]- which queries images based on their color, texture and shape, VisualSeek [5] - A Fully
Automated Content-Based Image Query System, SIMPLicity [6] - Semantics- Sensitive
Integrated Matching for Picture Libraries, and Blobworld [7] - Image segmentation using
expectation-maximization algorithm and applies the same to image querying.
2.2. Relevance Feedback
In order to narrow down the inconsistency of textual annotation and semantic gap of visual query,
the proposed approach uses relevance feedback in image retrieval. The basic Relevance Feedback
mechanism relies on iteratively asking the user to discriminate between relevant and irrelevant
images on a given set of results. A binary Relevance Feedback is used to train neural network
systems as in PicSOM [8] and in Bordogna and Pasi [9].
Applicability of Relevance feedback suffers from two vital problems - User interaction for
providing feedback is time consuming and it is a tiring process. In order to curb these cons,
automatic relevance feedback is used. This eliminates the user interaction totally in the feedback
process, thus saving time and energy to a great extent.
3. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, No.2, April 2012
179
2.3. Meta-Heuristics
In the last years, the development of optimization algorithms have been inspired and influenced
by natural and biological behaviours. Bio-inspired meta-heuristic optimization approaches
provided new ways to achieve nearly-optimal solutions in highly nonlinear, multidimensional
solution spaces, with lower complexity and faster convergence than traditional algorithms.
A bio-inspired stochastic optimization algorithm called Particle Swarm Optimization (PSO) was
introduced in the field of computational intelligence by Kennedy and Eberhart [10] in 1995. PSO
is a population based meta-heuristic optimization technique with stochastic nature, inspired by
social behaviour of bird flocking or fish schooling.
The system is initialised with a population of random solutions and searches for optima by
updating generations. The potential solutions, called particles, fly through the problem space by
following the current optimum particles. PSO has been successfully applied as an efficient
optimization tool in image classification [11].
3. PROPOSED APPROACH
This paper proposes an avant-garde approach of Meta-Heuristics Based ARF Optimization for
Image Retrieval, which avoids the semantic gap in image retrieval by combining automatic
relevance feedback and a stochastic algorithm. A visual feature database is constructed from the
image database, using combined feature vector calculated from colour moments, taking the mean,
standard deviation and skewness, and Gabor filter into account.
A combined feature vector is generated from colour moments (9 dimensions) & Gabor filters for
the query image. Visual feature database construction is done offline, and feature vector for query
image is constructed online. This way, the response time is not affected by the overhead of
feature vector computation for database images. Very few fast-computable features are included
in this step, because this is applied sequentially to all images present in the database.
Query image is mapped into a feature vector and the distance between the query and image is
calculated. The system ranks the whole dataset according to a minimum distance criterion. The
distance is the sum of weighted Euclidean distances between pairs of feature vectors – feature
vector of the database image and feature vector of the query image. The Weighted Euclidean
Distance WED(X,Y) is calculated as follows:
S
WED (X,Y) = 1/S . ∑ (Xs-Ys)2. Ws
k
----- (1)
s=1
where, X,Y are feature vectors corresponding to the query image and the images in the database,
S is the dimension of the feature vector, Ws
k
is the weight associated with feature vector, k is the
iteration number. If k=1, Ws
k
= 1 for all values of s.
For the first iteration, the weight of every feature is identical. From the second iteration, the
weight of each feature changes according to feature reweighing factor. This will be reflected in
the distance between the query image and the database images.
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Based on the computed distance, the nearest images are retrieved from the database and are
routed to automatic relevance feedback. Here the images are split into relevant and irrelevant
subsets.
Figure 1. Image Retrieval
For the retrieved images, first automatic relevance feedback based on Latent Semantic Indexing
(LSI) is generated. Here, LSI is applied in both textual and visual (image key) feature space. The
textual feature space is constructed by using the keywords related to the image.
If there is no associated keyword with an image, then textual feature space calculation is
automatically ignored by setting the value of α to 0.
TSim(q,i) = (q.i) / (|q| |i|) ----- (2)
where, q is term associated with the query image and i is the terms associated with the images in
the database.
The visual feature space comprises of a feature vector, which is a combination of Color histogram
bins (32 dimensions), Edge histogram bins (16 dimensions), wavelet texture energy values (18
dimensions).
VSim(q,i) = (q.i) / (|q| |i|) ----- (3)
where, q is feature vector of the query image and i is the feature vector of the image in the
database.
The combined similarity of textual and visual feature spaces is evaluated and the images are
labelled as relevant or irrelevant, based on the similarity value.
VTSim(q,i) = α . Tsim + (1- α) . Vsim ----- (4)
where, α is a constant value. To use only the textual feature space of the image, α value is set to 1,
and to use only the visual feature space of the image, when no keywords are associated with the
image, α value is set to 0. Here Tsim represents the textual similarity measure,
and Vsim represents visual similarity measure. Accordingly, relevant and irrelevant image subsets
are created, which will be progressively populated across iterations, based on the change in
weights of individual features, thus changing the distance between the query image and the
database images.
IMAGE
DATABASE
FEATURE
DATABASE
QUERY IMAGE VISUAL FEATURE
EXTRACTION
RETRIEVED
IMAGES
SIMILARITY
EVALUATION
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Figure 2. Automatic Relevance Feedback
3.1. Particle Swarm Optimizer
The feedback drives a feature re-weighting process [5] and is routed to the particle swarm
optimizer. The feature reweighting process determines the importance of individual features in
the retrieval process adaptively. Based on the reweighted feature value, the importance of each
feature is set in the forth- coming iterations.
Particle swarm optimizer is used to grasp image’s semantics through optimized iterative learning.
It provides a powerful optimization tool and an effective space exploration mechanism [6]. A
very preliminary version of PSO-CBIR was presented in [7]. After the relevant images are
computed through automatic relevance feedback, the swarm is initialized as follows: number of
swarm particles p, is initialized corresponding to the number of retrieved images, and each
particle is associated to one of the P nearest neighbours of the original query.
Personal best, i.e., pbest is the feature vector of the retrieved images associated to the particle.
Global best, i.e., gbest is the feature vector of query image. Each particle has its own pbest value,
and the gbest value is shared among all the particles in the swarm.
PSO’s fitness function represents the effectiveness of the solution reached by the swarm particles.
Fitness value of each particle is calculated and the swarm is ranked according to the lowest fitness
value first criterion. The image associated with the particle with least fitness value is more
relevant to the query image given by the user.
From the second iteration, the swarm evolves as follows: The position of the swarm particles is
updated in each iteration, starting from the second iteration. The position of the particle pn
k
is
given by,
pn
k
= pn
k
-1 + v n
k
----- (5)
where, pn
k
-1 is the position of the particle in previous iteration and vn
k
is the random speed
vector for the current iteration. The speed of the random vector keeps minimizing, as it reaches
convergence. This ensures deep exploration of more related images. The images corresponding to
the updated position of the particles are retrieved.
The gbest is updated as an image of the ‘relevant’ set. For every relevant image, the sum of
distances from other relevant images is calculated; then, the image resulting nearest to others is
chosen as gbest. The particles in the swarm, evolves in each iterations, by moving towards the
gbest.
RELEVANT IMAGES
IRRELEVANT IMAGES
TEXTUAL FEATURE
SPACE
VISUAL FEATURE
SPACE
COMBINED
SIMILARITY
EVALUATION
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After that, a new ranking is calculated based on the weighted Euclidean distance between the
query image and the images corresponding to the new position of the particles in the swarm.
Based on the updated swarm ranking, the nearest images are proposed to collect a new feedback
from automatic relevance feedback. The swarm is split as a multiple of two with respect to
current number of swarms. Each swarm now starts searching in parallel, which increases the
overall performance of the system. The updated results are taken to proceed with the next
iteration till convergence.
Figure 3. Particle Swarm Optimization
4. EXPERIMENTAL SETUP AND RESULTS
The experimental data comprises of collection of generic images from the Corel image database
(http://www.corel.com) [13], SIMPLicity[6], [14] and the web images. We prepared five classes
composed of 20 categories – (car, ship, cartoon, trucks), (birds, animals, sunflower, insects),
(desert, city night, mountains, farm lands), (kites, butterflies, rivers, sceneries) and (base ball,
basket ball, balls, play ground). The number is limited as compared to the huge number of images
used in large-scale applications, but it provides clear classification of images, so as to evaluate the
performance of the proposed method.
A combined feature vector is computed offline for the database images. This feature vector
semantically represents the images in the database. The query image is chosen by the user, and
the retrieval processes for the relevant images are triggered. Based on the Euclidean distance
between the combined feature vector, the relevant images are retrieved by the system and the first
iteration commences.
Figure4. Query Image Selection
FIRST
ITERATION
SWARM
INITIALIZATION
FITNESS
EVALUATION
No Yes
SWARM SPLIT
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The retrieved images are passed for automatic relevance feedback using Latent Semantic
Indexing. Here textual feature space and visual feature space are computed for the retrieved
images. The combined feature space is used to provide relevant and irrelevant subsets of images,
with respect to the query image.
Figure5. Set of retrieved images
In the Particle Swarm Optimizer, the particles are positioned corresponding to the first set of
retrieved images and swarm initialization is done. The fitness value of each particle is calculated.
The particle with least fitness value represents the best fit image for the query, and that is
maintained as global best for that iteration.
From the second iteration, the swarm is split into multiple of two, with respect to the current
number of swarms, with each swarm having its own global best value. From then on, the particles
start moving towards their corresponding swarm’s global best.
The position of the particles is updated in each iteration, based on a random vector value. The
random vector value [10] is calculated based on an inertial weight factor which is fixed in the
range [0.2,0.7] and two positive constants C1, C2 - called acceleration coefficients, aimed at
pulling the particle towards the position related to the personal best and global best (C1=C2=2).
The final automatic feedback results can be saved optionally, to make use of it in the future.
Figure6. Final Feedback saving for Retrieved Relevant Images
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The important difference of the proposed approach from typical optimization problems is that the
data are not completely available at the beginning, but they are collected from the automatic
feedback across iterations in an incremental manner.
As iterations progress, a user need input lesser information. This unsupervised version makes the
convergence faster, as compared to standard implementations, since the learning procedure is
driven automatically without any user intervention.
The performance of the system is projected in terms of precision and recall. Precision is
calculated as the number of retrieved relevant images over the feedback images and recall is
computed as percentage of relevant images retrieved across all iterations with respect to the
number of class samples.
The maximum average precision turns to be 0.80 and recall is 0.76. For all given classes of
images, the proposed approach gives similar and satisfactory results, which gives a clear picture
of the effectiveness of the approach for different classes of images. The following graph gives the
average precision - recall computation with the given classes.
5. CONCLUSION
The initial Image Retrieval uses fast and easily computable features, to reduce computational cost.
The features selected for Automatic Relevance Feedback are capable of retrieving in-depth low
level features of the image. Feature reweighing emphasizes the most discriminating parameters.
The image retrieval problem is formulated as an optimization problem, and is solved using
particle swarm optimization.
It takes into account the characteristics of relevant and irrelevant images, as points of attraction
and repulsion, thus performing an effective retrieval. The proposed approach achieves the
following goals without any human interaction – clustering relevant images using meta-heuristics
and dynamically modifies the feature space by feeding automatic relevance feedback.
REFERENCES
[1] Song Yan, “Research of Image Retrieval Based on color and texture feature”, Computer application
and soft computing, 2007, 9(2),pp.42-50.
[2] Ye Yu-guang, “Research of image retrieval based on fusing with multi-character”, Hua Qiao
university, 2007, pp.14-36.
Precision
Recall
Figure 7. Average Precision Vs Recall
9. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, No.2, April 2012
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[3] Y. Rui, T.S. Huang, and S. Mehrotra, “Relevance feedback a powerful tool in interactive content-
based image retrieval”,IEEE Transactions on Circuits and Systems for Video Technology, vol. 8, no.
5, pp. 644-655, 1998.
[4] W. Niblack, R. Barber, W. Equitz, M. Flickner, E. Glasman, D. Pektovic, P. Yanker, C. Faloutsos,
and G Taubin, "The QBIC project: Querying images by content using color, texture and shape,"
Proceedings of SPIE Storage and Retrieval for Image and Video Databases, San Jose, CA, 1994.
[5] J. R. Smith and S.-F. Chang, "VisualSEEk: A Fully Automated Content-Based Image Query System,"
Proceedings of the Fourth ACM International Conference on Multimedia '96, Boston, MA, 1996.
[6] J. Z. Wang, J. Li, and G. Wiederhold, "SIMPLIcity: Semantics- Sensitive Integrated Matching for
Picture LIbraries," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, pp. 947-
963, 2001.
[7] C. Carson, S. Belongie, H. Greenspan, and J. Malik, "Blobworld: Image segmentation using
expectation-maximization and its application to image querying," IEEE Transaction on Pattern
Analysis and Machine Intelligence.
[8] M. Koskela, J. Laaksonen, and E. Oja, “Use of image subsets in image retrieval with self-organizing
maps,” in Proc. Int. Conf. Image and Video Retrieval (CIVR), 2004, pp. 508–516.
[9] G. Bordogna and G. Pasi, “A user-adaptive neural network supporting a rule-based relevance
feedback,” Fuzzy Sets Syst., vol. 82, no. 9, pp. 201–211, 1996.
[10] J. Kennedy and R. C. Eberhart, “Particle swarm optimization,” in Proc. IEEE Conf. Neural Networks
IV, Piscataway, NJ, 1995.
[11] K. Chandramouli, “Particle swarm optimization and self-organizing maps based image classifier,” in
Proc. 2nd Int. Workshop Semantic Media Adaptation and Personalization, 2007, pp. 225–228.
[12] Tse-Wei Chen, Yi-Ling Chen, and Shao-Yi Chien , “Fast Image Segmentation and Texture Feature
Extraction for Image Retrieval”, 2009 IEEE 12th International Conference on Computer Vision
Workshops, ICCV Workshops.
[13] S. C. Hoi, M. R. Lyu, and R. Jin, “A unified log-based relevance feedback scheme for image
retrieval,” IEEE Transaction on Knowledge and Data Engineering, vol. 18, no. 4, pp. 509–524, Apr.
2006.
[14] Jia Li, James Z. Wang, ``Automatic linguistic indexing of pictures by a statistical modeling
approach,'' IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 9, pp. 1075-
1088, 2003.
Authors
Darsana B is working as a Senior Lecturer in The Oxford College of
Engineering, Bangalore. She has around 4 years of experience in this
profession. She has completed her B. Tech in Information Technology, M.E in
Computer Science and Engineering and currently pursuing her Ph D in the area
of Image Retrieval under the guidance of Dr. Rajan Chattamvelli. So far she has
presented 4 papers in different conferences and guiding different academic
projects related to Image Processing.
Rajan Chattamvelli has more than 15 years experience in software development,
teaching and research. He has taught computer science at various colleges and
universities in India and Cyprus. His areas of research include data mining,
algorithms design, information retrieval, object oriented technologies, parallel
computing, design patterns and statistics. He is the author of 3 books and more
than 16 research papers. He has also presented several papers at national and
international conferences. He can be contacted at cvrajan@gmail.com or
through skype at rajan.chattamvelli