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arXiv · 1402.5176

Pareto-depth for Multiple-query Image Retrieval

Ko-Jen Hsiao·Jeff Calder·Alfred O. Hero III
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Abstract

Most content-based image retrieval systems consider either one single query, or multiple queries that include the same object or represent the same semantic information. In this paper we consider the content-based image retrieval problem for multiple query images corresponding to different image semantics. We propose a novel multiple-query information retrieval algorithm that combines the Pareto front method (PFM) with efficient manifold ranking (EMR). We show that our proposed algorithm outperforms state of the art multiple-query retrieval algorithms on real-world image databases. We attribute this performance improvement to concavity properties of the Pareto fronts, and prove a theoretical result that characterizes the asymptotic concavity of the fronts.

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Ko-Jen Hsiao, Jeff Calder, Alfred O. Hero III. 2014-02-21. Pareto-depth for Multiple-query Image Retrieval. https://doi.org/10.1109/tip.2014.2378057

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