A variational inference based approach for image segmentation

In this paper, we present a variational Bayes (VB) approach for image segmentation. First, image is modeled by a mixture model, and then with the techniques of factor analyzer, the underlying structure of image content is inferred automatically. Different from the traditional EM algorithm that seriously suffers from component number selection, the proposed method can accurately infer the underlying image structure including suitable component number without usual sub- or over-segmentation problem. To overcome the problem of local optimization, a component split strategy is adopted in inference optimization process. Extensive experiments on various images validate the proposed method.