In this paper we present an unsupervised color image segmentation algorithm based on statistical models. We have adopted the Gaussian mixture model to represent the distribution of color feature vectors. A novel deterministic annealing EM and mean field theory from statistical mechanics are used to compute the posterior probability distribution of each pixel and estimate the parameters of the Gaussian Mixture Model. We describe the noncontexture segmentation algorithm that uses a deterministic annealing approach and the contexture segmentation algorithm that uses the mean field theory. The experimental results show that the deterministic annealing EM and mean field theory provide a global optimal solution for the maximum likelihood estimators and that these algorithms can efficiently segment the real image.