OPF-MRF: Optimum-path forest and Markov random fields for contextual-based image classification

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dc.contributor.authorNakamura, Rodrigo [UNESP]
dc.contributor.authorOsaku, Daniel
dc.contributor.authorLevada, Alexandre
dc.contributor.authorCappabianco, Fabio
dc.contributor.authorFalcão, Alexandre
dc.contributor.authorPapa, Joao [UNESP]
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.date.accessioned2014-05-27T11:30:45Z
dc.date.available2014-05-27T11:30:45Z
dc.date.issued2013-09-26
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.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.format.extent233-240
dc.identifierhttp://dx.doi.org/10.1007/978-3-642-40246-3_29
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.identifier.doi10.1007/978-3-642-40246-3_29
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.scopus2-s2.0-84884474442
dc.identifier.urihttp://hdl.handle.net/11449/76646
dc.language.isoeng
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.ispartofsjr0,295
dc.rights.accessRightsAcesso aberto
dc.sourceScopus
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.typeTrabalho apresentado em evento
dcterms.licensehttp://www.springer.com/open+access/authors+rights
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Ciências, Baurupt
unesp.departmentComputação - FCpt

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