Contextual Image Segmentation Based on the Potts Model

Image segmentation is one of the basic steps in image analysis. Clustering methods are an unsupervised way to provide image segmentation. This paper proposes a clustering algorithm for contextual image segmentation, called spatially variant finite mixture model (SVFMM). For the case of spatially varying mixture of Gaussian density functions with unknown means and variances, an expectation-maximization (EM) algorithm is derived for maximum likelihood estimation of the parameters of the mixture model. In this paper, the Potts model is adopted as a priori density function for the spatially variant mixture proportions to imposes spatial smoothness constraints in the model. Experimental results on a set of different real images show the effectiveness of the proposed method.