Bayesian example based segmentation using a hybrid energy model

Abstract

This paper describes a supervised segmentation algorithmwhich draws inspiration from recent advances in non-parametric texture synthesis. A set of example images which have been segmented a priori are used as a guide in the segmentation process. This new algorithm is built on the Bayesian framework and combines the strengths of both parametric and nonparametric modelling techniques. The suitability of the wavelet transform for texture modelling is highlighted and an outlier class condition is introduced as a means to increase the flexibility of the algorithm. Segmentation results demonstrate the potential of this new algorithm.