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Type: Artigo de periódico
Title: Exploiting clustering approaches for image re-ranking
Author: Pedronette, DCG
Torres, RD
Abstract: This paper presents the Distance Optimization Algorithm (DOA), a re-ranking method aiming to improve the effectiveness of Content-Based Image Retrieval (CBIR) systems. DOA considers an iterative clustering approach based on distances correlation and on the similarity of ranked lists. The algorithm explores the fact that if two images are similar, their distances to other images and therefore their ranked lists should be similar as well. We also describe how DOA can be used to combine different descriptors and then improve the quality of results of CBIR systems. Conducted experiments involving shape, color, and texture descriptors demonstrate the effectiveness of our method, when compared with state-of-the-art approaches. (C) 2011 Elsevier Ltd. All rights reserved.
Subject: Content-based image retrieval
Distance optimization
Country: Inglaterra
Editor: Academic Press Ltd- Elsevier Science Ltd
Citation: Journal Of Visual Languages And Computing. Academic Press Ltd- Elsevier Science Ltd, v. 22, n. 6, n. 453, n. 466, 2011.
Rights: fechado
Identifier DOI: 10.1016/j.jvlc.2011.08.001
Date Issue: 2011
Appears in Collections:Unicamp - Artigos e Outros Documentos

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