Variational Inference for Image Segmentation

Variational inference techniques are powerful methods for learning probabilistic models and provide significant advantages with respect to maximum likelihood (ML) or maximum a posteriori (MAP) approaches. Nevertheless they have not yet been fully exploited for image processing applications. In this paper we present a variational Bayes (VB) approach for image segmentation. We aim to show that VB provides a framework for generalizing existing segmentation algorithms that rely on an expectation-maximization formulation, while increasing their robustness and computational stability. We also show how optimal model complexity can be automatically determined in a variational setting, as opposed to ML frameworks which are intrinsically prone to overfitting.