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http://hdl.handle.net/10397/1894
Title: | Feature filtering in relevance feedback of image retrieval based on a statistical approach | Authors: | Fu, H Chi, ZG Feng, DD |
Issue Date: | 2004 | Source: | ISIMP 2004 : proceedings of 2004 International Symposium on Intelligent Multimedia, Video and Speech Processing : October 20-22, 2004, Hong Kong, p. 647-650 | Abstract: | Relevance feedback is a powerful tool to grasp the user's intention in image retrieval systems and has attracted many researchers' attention since 90's. In this paper, a feature filter whose parameters are computed by a statistical re-sampling approach is proposed in order to select the unique features to characterize the positive samples. A statistical voting procedure is then adopted to rank the candidates after getting rid of irrelevant feature components. Experimental results show that the proposed approach is more efficient and robust than the traditional method. | Keywords: | Content-based retrieval Feature extraction Filtering theory Image retrieval Image sampling Relevance feedback Statistical analysis |
Publisher: | IEEE | ISBN: | 0-7803-8687-6 | DOI: | 10.1109/ISIMP.2004.1434147 | Rights: | © 2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. |
Appears in Collections: | Conference Paper |
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Fu_Chi_Feng_Feature_Filtering_Feedback.pdf | 3.01 MB | Adobe PDF | View/Open |
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