Image Segmentation Using a Spatially Correlated Mixture Model with Gaussian Process Priors

Abstract

Finite mixture modeling has been widely used for image segmentation. However, since it takes no account of the spatial correlation among pixels in its standard form, its segmentation accuracy can be heavily deteriorated by noise in images. To improve segmentation accuracy in noisy images, the spatially variant finite mixture model has been proposed, in which a Markov Random Filed (MRF) is used as the prior for the mixing proportions and its parameters are estimated using the Expectation-Maximization (EM) algorithm based on the maximum a posteriori (MAP) criterion. In this paper, we propose a spatially correlated mixture model in which the mixing proportions are governed by a set of underlying functions whose common prior distribution is a Gaussian process. The spatial correlation can be expressed with a Gaussian process easily and flexibly. Given an image, the underlying functions are estimated by using a quasi EM algorithm and used to segment the image. The effectiveness of the proposed technique is demonstrated by an experiment with synthetic images.