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In this paper we present a statistical model-based approach to the color image segmentation. A novel deterministic annealing 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 ML parameter estimation and the real images are segmented efficiently using the estimates computed by the maximum entropy principle and men field theory.

Color Image Segmentation Using a Model-Based Clustering and a MFA-EM Algorithm