A Markov random field model for combining optimum-path forest classifiers using decision graphs and game strategy approach

• Autor(a) principal: Ponti Jr., Moacir P.
• Data de Publicação: 2011
• Outros Autores: Papa, João Paulo [UNESP], Levada, Alexandre L. M.
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1007/978-3-642-25085-9_69; http://hdl.handle.net/11449/72816
• topic: Classifier decisions; Decision graphs; Decision template; Ensemble of classifiers; Final decision; Forest classifiers; Game strategies; Markov Random Field model; Multiple classifier systems; Multiple classifiers systems; Random field model; Real data sets; Computer simulation; Computer vision; Forestry; Image segmentation; Pattern recognition systems; Classifiers; Computers; Image Analysis; OCR; Patterns; Random Processes; Segmentation; Simulation; Vision

The research on multiple classifiers systems includes the creation of an ensemble of classifiers and the proper combination of the decisions. In order to combine the decisions given by classifiers, methods related to fixed rules and decision templates are often used. Therefore, the influence and relationship between classifier decisions are often not considered in the combination schemes. In this paper we propose a framework to combine classifiers using a decision graph under a random field model and a game strategy approach to obtain the final decision. The results of combining Optimum-Path Forest (OPF) classifiers using the proposed model are reported, obtaining good performance in experiments using simulated and real data sets. The results encourage the combination of OPF ensembles and the framework to design multiple classifier systems. © 2011 Springer-Verlag.