A Spatially Constrained Asymmetric Gaussian Mixture Model for Image Segmentation

Abstract

Gaussian mixture models with spatial constraint play an important role in image segmentation. Nevertheless, most methods suffer from one or more challenges such as limited robustness to outliers, over-smoothness for segmentations, and lack of flexibility to fit different shapes of observed data. To address above issues, in this paper, we propose a spatially constrained asymmetric Gaussian mixture model for image segmentation. The asymmetric distribution is utilized to fit different shapes of observed data. Then our asymmetric model can be constructed based on the posterior and prior probabilities of within-cluster and between-cluster. Moreover, we introduce two pseudo likelihood quantities which respectively couple neighboring priors of within-cluster and between-cluster based on the Kullback-Leibler divergence. Finally, we derive an expectation maximization algorithm to iteratively maximize the approximation of the lower bound of the data log-likelihood. Experimental results on synthetic and real images demonstrate the superior performance of the proposed algorithm comparing with state-of-the-art segmentation approaches.