OPF-MRF: Optimum-path forest and Markov random fields for contextual-based image classification
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Data
2013-09-26
Autores
Nakamura, Rodrigo [UNESP]
Osaku, Daniel
Levada, Alexandre
Cappabianco, Fabio
Falcão, Alexandre
Papa, Joao [UNESP]
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Resumo
Some 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.
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Contextual Classification, Markov Random Fields, Optimum-Path Forest
Como citar
Lecture 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.