Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/69231
Type: Artigo de periódico
Title: Image re-ranking and rank aggregation based on similarity of ranked lists
Author: Pedronette, DCG
Torres, RD
Abstract: In Content-based Image Retrieval (CBIR) systems, ranking accurately collection images is of great relevance. Users are interested in the returned images placed at the first positions, which usually are the most relevant ones. Collection images are ranked in increasing order of their distance to the query pattern (e.g., query image) defined by users. Therefore, the effectiveness of these systems is very dependent on the accuracy of the distance function adopted. In this paper, we present a novel context-based approach for redefining distances and later re-ranking images aiming to improve the effectiveness of CBIR systems. In our approach, distances among images are redefined based on the similarity of their ranked lists. Conducted experiments involving shape, color, and texture descriptors demonstrate the effectiveness of our method. (C) 2013 Elsevier Ltd. All rights reserved.
Subject: Content-based image retrieval
Re-ranking
Ranked lists
Rank aggregation
Country: Inglaterra
Editor: Elsevier Sci Ltd
Rights: fechado
Identifier DOI: 10.1016/j.patcog.2013.01.004
Date Issue: 2013
Appears in Collections:Unicamp - Artigos e Outros Documentos

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