Please use this identifier to cite or link to this item:
- OPF-MRF: Optimum-path forest and Markov random fields for contextual-based image classification
- Universidade Estadual Paulista (UNESP)
- Universidade Federal de São Carlos (UFSCar)
- Universidade Federal de São Paulo (UNIFESP)
- Universidade Estadual de Campinas (UNICAMP)
- 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.
- Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 8048 LNCS, n. PART 2, p. 233-240, 2013.
- Contextual Classification
- Markov Random Fields
- Optimum-Path Forest
- Acesso restrito
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.