Bayesian Example Based Segmentation using a Hybrid Energy Model
Claire Gallagher, Anil C. Kokaram · 2007
This paper describes a supervised segmentation algorithm which 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 non-parametric 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.