Bayesian image segmentations by Potts prior and loopy belief propagation
This work addresses hyperparameter estimation in image segmentation for computer vision applications, but it appears incremental as it builds on existing Bayesian models and loopy belief propagation methods.
The paper tackles the problem of determining hyperparameters in Bayesian image segmentation models by proposing a new conditional maximum entropy estimation scheme for Potts priors, showing how it compares to conventional maximum likelihood methods and clarifying the influence of phase transitions in loopy belief propagation.
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 LBP's for Potts models influence our hyperparameter estimation procedures.