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dc.contributor.authorMansano, A.-
dc.contributor.authorMatsuoka, J. A.-
dc.contributor.authorAfonso, L. C S-
dc.contributor.authorPapa, João Paulo-
dc.contributor.authorFaria, F.-
dc.contributor.authorDa, R.-
dc.identifier.citationBrazilian Symposium of Computer Graphic and Image Processing, p. 324-329.-
dc.description.abstractThe efficiency in image classification tasks can be improved using combined information provided by several sources, such as shape, color, and texture visual properties. Although many works proposed to combine different feature vectors, we model the descriptor combination as an optimization problem to be addressed by evolutionary-based techniques, which compute distances between samples that maximize their separability in the feature space. The robustness of the proposed technique is assessed by the Optimum-Path Forest classifier. Experiments showed that the proposed methodology can outperform individual information provided by single descriptors in well-known public datasets. © 2012 IEEE.en
dc.subjectDescriptor Combination-
dc.subjectEvolutionary algorithms-
dc.subjectImage classification-
dc.subjectCombined informations-
dc.subjectData sets-
dc.subjectFeature space-
dc.subjectFeature vectors-
dc.subjectOptimization problems-
dc.subjectOptimum-path forests-
dc.subjectVisual properties-
dc.subjectVector spaces-
dc.titleImproving image classification through descriptor combinationen
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)-
dc.contributor.institutionUniversidade Estadual de Campinas (UNICAMP)-
dc.description.affiliationDepartment of Computing São Paulo State University, Bauru-
dc.description.affiliationInstitute of Computing University of Campinas, Campinas, SP-
dc.description.affiliationUnespDepartment of Computing São Paulo State University, Bauru-
dc.rights.accessRightsAcesso restrito-
dc.relation.ispartofBrazilian Symposium of Computer Graphic and Image Processing-
Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

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