Relevance feedback in image retrieval: A comprehensive review |
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Authors: | Xiang Sean Zhou Thomas S. Huang |
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Affiliation: | (1) Siemens Corporate Research 755 College Road East, Princeton, NJ 08540, USA; e-mail: xzhou@scr.siemens.com , US;(2) Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana Champaign, 405 N Mathews Ave, Urbana, IL 61801, USA; e-mail: huang@ifp.uiuc.edu , US |
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Abstract: | ![]() We analyze the nature of the relevance feedback problem in a continuous representation space in the context of content-based image retrieval. Emphasis is put on exploring the uniqueness of the problem and comparing the assumptions, implementations, and merits of various solutions in the literature. An attempt is made to compile a list of critical issues to consider when designing a relevance feedback algorithm. With a comprehensive review as the main portion, this paper also offers some novel solutions and perspectives throughout the discussion. RID="*" ID="*" Work was done while at the University of Illinois. |
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Keywords: | : Relevance feedback – Content-based image retrieval – Computer vision – Classification – Pattern recognition – Small sample learning |
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