OPF-MRF: Optimum-path forest and Markov random fields for contextual-based image classification - Nakamura, Rodrigo [UNESP]; Osaku, Daniel; Levada, Alexandre; Cappabianco, Fabio; Falcão, Alexandre; Papa, Joao [UNESP] (Springer Nature, 2013)
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.
Keywords: Contextual Classification; Markov Random Fields; Optimum-Path Forest