Model-Based segmentation of image data using spatially constrained mixture models

In this paper, a novel Bayesian statistical approach is proposed to tackle the problem of natural image segmentation. The proposed approach is based on finite Dirichlet mixture models in which contextual proportions (i.e., the probabilities of class labels) are modeled with spatial smoothness constraints. The major merits of our approach are summarized as follows: Firstly, it exploits the Dirichlet mixture model which can obtain a better statistical performance than commonly used mixture models (such as the Gaussian mixture model), especially for proportional data (i.e, normalized histogram). Secondly, it explicitly models the mixing contextual proportions as probability vectors and simultaneously integrate spatial relationship between pixels into the Dirichlet mixture model, which results in a more robust framework for image segmentation. Finally, we develop a variational Bayes learning method to update the parameters in a closed-form expression. The effectiveness of the proposed approach is compared with other mixture modeling-based image segmentation approaches through extensive experiments that involve both simulated and natural color images.