Majorization-minimization mixture model determination in image segmentation

Abstract

A new Bayesian model for image segmentation based on a Gaussian mixture model is proposed. The model structure allows the automatic determination of the number of segments while ensuring spatial smoothness of the final output. This is achieved by defining two separate mixture weight sets: the first set of weights is spatially variant and incorporates an MRF edge-preserving smoothing prior; the second set of weights is governed by a Dirichlet prior in order to prune unnecessary mixture components. The model is trained using variational inference and the Majorization-Minimization (MM) algorithm, resulting in closed-form parameter updates. The algorithm was successfully evaluated in terms of various segmentation indices using the Berkeley image data base.