A Block-based Markov Random Field Model Estimation for Contextual Classification Using Optimum-Path Forest
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Data
2016-01-01
Autores
Osaku, Daniel
Levada, Alexandre L. M.
Papa, Joao Paulo [UNESP]
IEEE
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Editor
Ieee
Resumo
Contextual image classification aims at considering the information about nearby samples in the learning process in order to provide more accurate results. In this paper, we propose a locally-adaptive Optimum-Path Forest classifier together with Markov Random Fields (MRF) that surpasses its naive version, which was recently presented in the literature. The experimental results over four satellite images demonstrated the proposed approach can outperform previous results, as well as it can perform MRF parameter learning much faster than its former version.Contextual image classification aims at considering the information about nearby samples in the learning process in order to provide more accurate results. In this paper, we propose a locally-adaptive Optimum-Path Forest classifier together with Markov Random Fields (MRF) that surpasses its naive version, which was recently presented in the literature. The experimental results over four satellite images demonstrated the proposed approach can outperform previous results, as well as it can perform MRF parameter learning much faster than its former version.
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Palavras-chave
Pattern Classification, Optimum-Path Forest, Land-cover Classificaton
Como citar
2016 Ieee International Symposium On Circuits And Systems (iscas). New York: Ieee, p. 994-997, 2016.