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An Image Clustering and Feedback-based Retrieval Framework

An Image Clustering and Feedback-based Retrieval Framework

Chengcui Zhang, Liping Zhou, Wen Wan, Jeffrey Birch, Wei-Bang Chen
Copyright: © 2010 |Volume: 1 |Issue: 1 |Pages: 20
ISSN: 1947-8534|EISSN: 1947-8542|ISSN: 1947-8534|EISBN13: 9781616929954|EISSN: 1947-8542|DOI: 10.4018/jmdem.2010111204
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MLA

Zhang, Chengcui, et al. "An Image Clustering and Feedback-based Retrieval Framework." IJMDEM vol.1, no.1 2010: pp.55-74. http://doi.org/10.4018/jmdem.2010111204

APA

Zhang, C., Zhou, L., Wan, W., Birch, J., & Chen, W. (2010). An Image Clustering and Feedback-based Retrieval Framework. International Journal of Multimedia Data Engineering and Management (IJMDEM), 1(1), 55-74. http://doi.org/10.4018/jmdem.2010111204

Chicago

Zhang, Chengcui, et al. "An Image Clustering and Feedback-based Retrieval Framework," International Journal of Multimedia Data Engineering and Management (IJMDEM) 1, no.1: 55-74. http://doi.org/10.4018/jmdem.2010111204

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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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