A Block-based Markov Random Field Model Estimation for Contextual Classification Using Optimum-Path Forest

• Autor(a) principal: Osaku, Daniel
• Data de Publicação: 2016
• Outros Autores: Levada, Alexandre L. M., Papa, Joao Paulo [UNESP], IEEE
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://hdl.handle.net/11449/165406
• topic: Pattern Classification; Optimum-Path Forest; Land-cover Classificaton

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.