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Type: Artigo de periódico
Title: Incorporating multiple distance spaces in optimum-path forest classification to improve feedback-based learning
Author: da Silva, AT
dos Santos, JA
Falcao, AX
Torres, RD
Magalhaes, LP
Abstract: In content-based image retrieval (CBIR) using feedback-based learning, the user marks the relevance of returned images and the system learns how to return more relevant images in a next iteration. In this learning process, image comparison may be based on distinct distance spaces due to multiple visual content representations. This work improves the retrieval process by incorporating multiple distance spaces in a recent method based on optimum-path forest (OPF) classification. For a given training set with relevant and irrelevant images, an optimization algorithm finds the best distance function to compare images as a combination of their distances according to different representations. Two optimization techniques are evaluated: a multi-scale parameter search (MSPS), never used before for CBIR, and a genetic programming (GP) algorithm. The combined distance function is used to project an OPF classifier and to rank images classified as relevant for the next iteration. The ranking process takes into account relevant and irrelevant representatives, previously found by the OPF classifier. Experiments show the advantages in effectiveness of the proposed approach with both optimization techniques over the same approach with single distance space and over another state-of-the-art method based on multiple distance spaces. Crown Copyright (C) 2011 Published by Elsevier Inc. All rights reserved.
Subject: Content-based image retrieval
Optimum-path forest classifiers
Composite descriptor
Genetic programming
Multi-scale parameter search
Image pattern analysis
Country: EUA
Editor: Academic Press Inc Elsevier Science
Citation: Computer Vision And Image Understanding. Academic Press Inc Elsevier Science, v. 116, n. 4, n. 510, n. 523, 2012.
Rights: fechado
Identifier DOI: 10.1016/j.cviu.2011.12.001
Date Issue: 2012
Appears in Collections:Unicamp - Artigos e Outros Documentos

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