OPF-MRF: Optimum-Path Forest and Markov Random Fields for Contextual-Based Image Classification
International Conference on Computer Analysis of Images and Patterns(2013)
摘要
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.
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关键词
Optimum-Path Forest,Markov Random Fields,Contextual Classification
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