Content based image retrieval (CBIR), also known as query by image content (QBIC) is the applicat... more Content based image retrieval (CBIR), also known as query by image content (QBIC) is the application of computer vision to the image retrieval problem, that is, the problem of searching for digital images in large databases. Users of image databases often prefer to retrieve relevant images by categories. Unfortunately, images are usually indexed by content or low level features like color, texture and shape, which often fail to capture high level concepts well. Again the high level concepts or semantics can vary from user to user. To address these issues, relevance feedback has been extensively used to associate low level image features with high level concepts. Because it is hard to define what will be the most similar images of a query image as it varies from a user to another, taking user judgement on the retrieved images and then refining the search result based on user’s region of interest has become very successful. Many researchers have addressed the issues of Content based Image retrieval with relevance feedback and contributed in this field to a considerable extent. Still there are scope and need of much improvement. Among all existing relevance feedback approaches, query point movement and feature re-weighting have been proven to be suitable for large-scaled image databases with high dimensional image features. In this thesis, we present a query point movement approach using both relevant and irrelevant images to catch the user semantics. The relevance feedback strategy is simple and straightforward. The basic approach adopted here is to move the query point to the direction of user intention. The user only selects the images that he/she thinks relevant and ignores the irrelevant ones. The feedback provided by the user are categorized into positive and negatives images both of which categories are used in determining the query point for further iterations. During experimentation it is observed that our system can efficiently move towards the user context with minimum possible number of iterations comparing to trivial methods.
Content based image retrieval (CBIR), also known as query by image content (QBIC) is the applicat... more Content based image retrieval (CBIR), also known as query by image content (QBIC) is the application of computer vision to the image retrieval problem, that is, the problem of searching for digital images in large databases. Users of image databases often prefer to retrieve relevant images by categories. Unfortunately, images are usually indexed by content or low level features like color, texture and shape, which often fail to capture high level concepts well. Again the high level concepts or semantics can vary from user to user. To address these issues, relevance feedback has been extensively used to associate low level image features with high level concepts. Because it is hard to define what will be the most similar images of a query image as it varies from a user to another, taking user judgement on the retrieved images and then refining the search result based on user’s region of interest has become very successful. Many researchers have addressed the issues of Content based Image retrieval with relevance feedback and contributed in this field to a considerable extent. Still there are scope and need of much improvement. Among all existing relevance feedback approaches, query point movement and feature re-weighting have been proven to be suitable for large-scaled image databases with high dimensional image features. In this thesis, we present a query point movement approach using both relevant and irrelevant images to catch the user semantics. The relevance feedback strategy is simple and straightforward. The basic approach adopted here is to move the query point to the direction of user intention. The user only selects the images that he/she thinks relevant and ignores the irrelevant ones. The feedback provided by the user are categorized into positive and negatives images both of which categories are used in determining the query point for further iterations. During experimentation it is observed that our system can efficiently move towards the user context with minimum possible number of iterations comparing to trivial methods.
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Papers by Md Abdul Motaleb Faysal
retrieve relevant images by categories. Unfortunately, images are usually indexed by content or low level features like color, texture and shape, which often fail to capture high level concepts well. Again the high level concepts or semantics can vary from user to user. To address these issues, relevance feedback has been extensively used to associate low level image features with high level concepts. Because it is hard to define what will be the most similar images of a query image as it varies from a user to another, taking user judgement on the retrieved images and then refining the search result based on user’s region of interest has become very successful. Many researchers have addressed the issues of Content based Image retrieval with relevance feedback and contributed in this field to a considerable extent. Still there are scope and need of much improvement. Among all existing relevance feedback approaches, query point movement and feature re-weighting have been proven to be suitable for large-scaled image databases with high dimensional image features. In this thesis, we present a query point movement approach using both relevant and irrelevant images to catch the user semantics. The relevance feedback strategy is simple and straightforward. The basic approach adopted here is to move the query point to the direction of user intention. The user only selects the images that he/she thinks relevant and
ignores the irrelevant ones. The feedback provided by the user are categorized into positive and negatives images both of which categories are used in determining the query point for further iterations. During experimentation it is observed that our system can efficiently move towards the user context with minimum possible number of iterations comparing to trivial methods.
retrieve relevant images by categories. Unfortunately, images are usually indexed by content or low level features like color, texture and shape, which often fail to capture high level concepts well. Again the high level concepts or semantics can vary from user to user. To address these issues, relevance feedback has been extensively used to associate low level image features with high level concepts. Because it is hard to define what will be the most similar images of a query image as it varies from a user to another, taking user judgement on the retrieved images and then refining the search result based on user’s region of interest has become very successful. Many researchers have addressed the issues of Content based Image retrieval with relevance feedback and contributed in this field to a considerable extent. Still there are scope and need of much improvement. Among all existing relevance feedback approaches, query point movement and feature re-weighting have been proven to be suitable for large-scaled image databases with high dimensional image features. In this thesis, we present a query point movement approach using both relevant and irrelevant images to catch the user semantics. The relevance feedback strategy is simple and straightforward. The basic approach adopted here is to move the query point to the direction of user intention. The user only selects the images that he/she thinks relevant and
ignores the irrelevant ones. The feedback provided by the user are categorized into positive and negatives images both of which categories are used in determining the query point for further iterations. During experimentation it is observed that our system can efficiently move towards the user context with minimum possible number of iterations comparing to trivial methods.