Mean Field Decomposition of a Posteriori Probability for MRF-Based Image Segmentation: Unsupervised Multispectral Textured Image Segmentation

Hideki NODA  Mehdi N. SHIRAZI  Bing ZHANG  Nobuteru TAKAO  Eiji KAWAGUCHI  

IEICE TRANSACTIONS on Information and Systems   Vol.E82-D   No.12   pp.1605-1611
Publication Date: 1999/12/25
Online ISSN: 
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Image Processing,Computer Graphics and Pattern Recognition
image segmentation,  MRF,  mean field,  texture,  unsupervised,  

Full Text: PDF>>
Buy this Article

This paper proposes a Markov random field (MRF) model-based method for unsupervised segmentation of multispectral images consisting of multiple textures. To model such textured images, a hierarchical MRF is used with two layers, the first layer representing an unobservable region image and the second layer representing multiple textures which cover each region. This method uses the Expectation and Maximization (EM) method for model parameter estimation, where in order to overcome the well-noticed computational problem in the expectation step, we approximate the Baum function using mean-field-based decomposition of a posteriori probability. Given provisionally estimated parameters at each iteration in the EM method, a provisional segmentation is carried out using local a posteriori probability (LAP) of each pixel's region label, which is derived by mean-field-based decomposition of a posteriori probability of the whole region image. Experiments show that the use of LAPs is essential to perform a good image segmentation.