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Type: Artigo de periódico
Title: Relevance feedback based on genetic programming for image retrieval
Author: Ferreira, CD
Santos, JA
Torres, RD
Goncalves, MA
Rezende, RC
Fan, WG
Abstract: This paper presents two content-based image retrieval frameworks with relevance feedback based on genetic programming. The first framework exploits only the user indication of relevant images. The second one considers not only the relevant but also the images indicated as non-relevant. Several experiments were conducted to validate the proposed frameworks. These experiments employed three different image databases and color, shape, and texture descriptors to represent the content of database images. The proposed frameworks were compared, and outperformed six other relevance feedback methods regarding their effectiveness and efficiency in image retrieval tasks. (C) 2010 Elsevier B.V. All rights reserved.
Subject: Relevance feedback
Content-based image retrieval
Genetic programming
Country: Holanda
Editor: Elsevier Science Bv
Citation: Pattern Recognition Letters. Elsevier Science Bv, v. 32, n. 1, n. 27, n. 37, 2011.
Rights: fechado
Identifier DOI: 10.1016/j.patrec.2010.05.015
Date Issue: 2011
Appears in Collections:Unicamp - Artigos e Outros Documentos

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