Mean field annealing EM for image segmentation

We present a statistical model-based approach to the color image segmentation. A novel deterministic annealing expectation-maximization (EM) and mean field theory are used to estimate the posterior probability of each pixel and the parameters of the Gaussian mixture model which represents the multi-colored objects statistically. Image segmentation is carried out by clustering each pixel into the most probable component Gaussian. The experimental results show that the mean field annealing EM provides a global optimal solution for the maximum likelihood parameter estimation and the real images are segmented efficiently using the estimates computed by the maximum entropy principle and mean field theory