IEEE Trans Image Process - Pareto-depth for multiple-query image retrieval.

Tópicos

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{ problem(2511) optim(1539) algorithm(950) }
{ imag(2830) propos(1344) filter(1198) }
{ data(3008) multipl(1320) sourc(1022) }
{ search(2224) databas(1162) retriev(909) }
{ method(2212) result(1239) propos(1039) }
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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

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.

Resumo Limpo

contentbas imag retriev system consid either one singl queri multipl queri includ object repres semant inform paper consid contentbas imag retriev problem multipl queri imag correspond differ imag semant propos novel multiplequeri inform retriev algorithm combin pareto front method effici manifold rank show propos algorithm outperform state art multiplequeri retriev algorithm realworld imag databas attribut perform improv concav properti pareto front prove theoret result character asymptot concav front

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