Texture image segmentation using hierarchical MRF model based on the interactive potential function and mean-field parameter estimation

To solve the difficult problem of expectations caused by interaction between hidden variables when using Expectation-maximization (EM) algorithm to estimate parameters for hierarchical Markov Random Fields (MRF) model, the mean-field theory is introduced into Gaussian-MRF (GMRF) model. Parameters can be estimated easily through simple linear equation in case of without window function. An interactive potential function based on Bayesian belief propagation algorithm is proposed to change the situation that the fixed or variable weighted potential function can not express the interaction of image regions. Experiments demonstrate that the proposed method not only has good regional classification but also smoothly internal region. In addition, the mixed and confused phenomenon of traditional hierarchical MRF is improved in wavelet domain.