An automatic segmentation combining mixture analysis and adaptive region information: A level set approach

In this paper we propose a novel automatic framework for variational color image segmentation based on unifying adaptive region information and mixture modelling. We consider a formulation of the region information by using the posterior probability of a mixture of General Gaussian (GG) pd/s, where each region is represented by a pdf. The segmentation is formulated by the minimization of an energy functional according to the region contours and all the mixture parameters respectively. Two main objectives are achieved by the approach. A scheme is provided to extend easily the adaptive segmentation to an arbitrary number of regions and to perform it in a fully automatic fashion. Moreover, the segmentation recovers an accurate and representative mixture of pd/s. In the approach, we couple the boundary and region information of the image to steer the segmentation. We validate the method on the segmentation of real world color images.