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.
CITATION STYLE
Nakamura, R., Osaku, D., Levada, A., Cappabianco, F., Falcão, A., & Papa, J. (2013). OPF-MRF: Optimum-path forest and Markov random fields for contextual-based image classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8048 LNCS, pp. 233–240). https://doi.org/10.1007/978-3-642-40246-3_29
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