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) pdfs, 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 pdfs. 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.