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An Image Clustering and Feedback-Based Retrieval Framework
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Author(s): Chengcui Zhang (University of Alabama at Birmingham, USA), Liping Zhou (University of Alabama at Birmingham, USA), Wen Wan (University of Alabama at Birmingham, USA), Jeffrey Birch (Virginia Polytechnic Institute and State University, USA)and Wei-Bang Chen (University of Alabama at Birmingham, USA)
Copyright: 2012
Pages: 19
Source title:
Methods and Innovations for Multimedia Database Content Management
Source Author(s)/Editor(s): Shu-Ching Chen (University of Missouri-Kansas City, United States)and Mei-Ling Shyu (University of Miami, USA)
DOI: 10.4018/978-1-4666-1791-9.ch005
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Abstract
Most existing object-based image retrieval systems are based on single object matching, with its main limitation being that one individual image region (object) can hardly represent the user’s retrieval target, especially when more than one object of interest is involved in the retrieval. Integrated Region Matching (IRM) has been used to improve the retrieval accuracy by evaluating the overall similarity between images and incorporating the properties of all the regions in the images. However, IRM does not take the user’s preferred regions into account and has undesirable time complexity. In this article, we present a Feedback-based Image Clustering and Retrieval Framework (FIRM) using a novel image clustering algorithm and integrating it with Integrated Region Matching (IRM) and Relevance Feedback (RF). The performance of the system is evaluated on a large image database, demonstrating the effectiveness of our framework in catching users’ retrieval interests in object-based image retrieval.
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