A Bayesian segmentation framework for textured visual images

This paper presents a new framework for segmentation of textured visual imagery. The proposed method consists of a Bayesian formulation for labeling similar regions. Similarity is defined via texture features obtained by Gabor Wavelets. Multivariate Gaussian distributions are employed to model the feature class-conditional densities, while the Markov process is used to characterize the distributions of the region labeling due to each feature. A coarse nearest neighbor clustering is performed over the feature space to estimate the initial labelings. An iterative solution to the Maximum A Posteriori (MAP) estimation is developed, where the parameters of the prior distribution of region labels are estimated using the Expectation-Maximization (EM) algorithm. Finally, for man-made object segmentation, a region-growing procedure is used to analyze the classified texture regions by incorporating measures of local shape characteristics to obtain smooth boundaries and region homogeneity. Results of the developed algorithm on real scene images are presented