Bayesian image segmentations by potts prior and loopy belief propagation

Kazuyuki Tanaka, Shun Kataoka, Muneki Yasuda, Yuji Waizumi, Chiou Ting Hsu

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


This paper presents a Bayesian image segmentation model based on Potts prior and loopy belief propagation. The proposed Bayesian model involves several terms, including the pairwise interactions of Potts models, and the average vectors and covariant matrices of Gauss distributions in color image modeling. These terms are often referred to as hyperparameters in statistical machine learning theory. In order to determine these hyperparameters, we propose a new scheme for hyperparameter estimation based on conditional maximization of entropy in the Potts prior. The algorithm is given based on loopy belief propagation. In addition, we compare our conditional maximum entropy framework with the conventional maximum likelihood framework, and also clarify how the first order phase transitions in loopy belief propagations for Potts models influence our hyperparameter estimation procedures.

Original languageEnglish
Article number124002
JournalJournal of the Physical Society of Japan
Issue number12
Publication statusPublished - 2014 Dec 15


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