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Proceedings Paper

Decision-theoretic image retrieval
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Paper Abstract

The design of an effective architecture for image retrieval requires careful consideration of the interplay between the three major components of a retrieval system: feature transformation, feature representation, and similarity function. We present a review of ongoing work on a decision theoretic formulation of the retrieval problem that enables the design of systems where all components are optimized with respect to the same end-to-end performance criteria: the minimization of the probability of retrieval error. In addition to some previously published results on the theoretical characterization of the impact of the feature transformation and representation in the probability of error, we present an efficient algorithm for optimal feature selection. Experimental results show that decision-theoretic retrieval performs well on color, texture, and generic image databases in terms of both retrieval accuracy and perceptual relevance of similarity judgments.

Paper Details

Date Published: 1 July 2002
PDF: 12 pages
Proc. SPIE 4862, Internet Multimedia Management Systems III, (1 July 2002); doi: 10.1117/12.473028
Show Author Affiliations
Nuno Miguel Vasconcelos, Hewlett-Packard Lab. (United States)


Published in SPIE Proceedings Vol. 4862:
Internet Multimedia Management Systems III
John R. Smith; Sethuraman Panchanathan; Tong Zhang, Editor(s)

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