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Published in:
2013 | OriginalPaper | Chapter
OPF-MRF: Optimum-Path Forest and Markov Random Fields for Contextual-Based Image Classification
Authors : Rodrigo Nakamura, Daniel Osaku, Alexandre Levada, Fabio Cappabianco, Alexandre Falcão, Joao Papa
Published in: Computer Analysis of Images and Patterns
Publisher: Springer Berlin Heidelberg
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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.