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dc.contributor.authorNakamura, Rodrigo-
dc.contributor.authorOsaku, Daniel-
dc.contributor.authorLevada, Alexandre-
dc.contributor.authorCappabianco, Fabio-
dc.contributor.authorFalcão, Alexandre-
dc.contributor.authorPapa, Joao-
dc.identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 8048 LNCS, n. PART 2, p. 233-240, 2013.-
dc.description.abstractSome machine learning methods do not exploit contextual information in the process of discovering, describing and recognizing patterns. However, spatial/temporal neighboring samples are likely to have same behavior. Here, we propose an approach which unifies a supervised learning algorithm - namely Optimum-Path Forest - together with a Markov Random Field in order to build a prior model holding a spatial smoothness assumption, which takes into account the contextual information for classification purposes. We show its robustness for brain tissue classification over some images of the well-known dataset IBSR. © 2013 Springer-Verlag.en
dc.subjectContextual Classification-
dc.subjectMarkov Random Fields-
dc.subjectOptimum-Path Forest-
dc.titleOPF-MRF: Optimum-path forest and Markov random fields for contextual-based image classificationen
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)-
dc.contributor.institutionUniversidade Federal de São Carlos (UFSCar)-
dc.contributor.institutionUniversidade Federal de São Paulo (UNIFESP)-
dc.contributor.institutionUniversidade Estadual de Campinas (UNICAMP)-
dc.description.affiliationUNESP - Univ. Estadual Paulista Department of Computing, Bauru-SP-
dc.description.affiliationDepartment of Computing Federal University of São Carlos-
dc.description.affiliationInstitute of Science and Technology Federal University of São Paulo-
dc.description.affiliationInstitute of Computing University of Campinas-
dc.description.affiliationUnespUNESP - Univ. Estadual Paulista Department of Computing, Bauru-SP-
dc.rights.accessRightsAcesso restrito-
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

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