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Pareto-Depth for Multiple-Query Image Retrieval
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DOI：10.1109/TIP.2014.2378057.png)
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摘要
En 中文
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 with efficient manifold ranking. 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.
Keyword:
Pareto fronts
information retrieval
multiple-query retrieval
manifold ranking
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期刊
IF:
13.7
论文数:
1.0W
被引数:
8.4W
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