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Type: Artigo de periódico
Title: Using contextual spaces for image re-ranking and rank aggregation
Author: Pedronette, DCG
Torres, RD
Calumby, RT
Abstract: This article presents two novel re-ranking approaches that take into account contextual information defined by the K-Nearest Neighbours (KNN) of a query image for improving the effectiveness of CBIR systems. The main contributions of this article are the definition of the concept of contextual spaces for encoding contextual information of images; the definition of two new re-ranking algorithms that exploit contextual information encoded in contextual spaces; and the evaluation of the proposed algorithms in several CBIR tasks related to the combination of image descriptors; combination of visual and textual descriptors; and combination of post-processing (re-ranking) methods. We conducted a large evaluation protocol involving visual descriptors (considering shape, color, and texture) and textual descriptors, various datasets, and comparisons with other post-processing methods. Experimental results demonstrate the effectiveness of our approaches.
Subject: Content-based image retrieval
Rank aggregation
Contextual information
Multimodal retrieval
Country: Holanda
Editor: Springer
Rights: fechado
Identifier DOI: 10.1007/s11042-012-1115-z
Date Issue: 2014
Appears in Collections:Unicamp - Artigos e Outros Documentos

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