Shape priors and MRF-segmentation

Nataliya Sokolovska
(Telecom ParisTech)

Discriminative probabilistic models design directly conditional probability of a class given an observation. The logistic regression has been widely used due to its simplicity and effectiveness. Conditional random fields allow to take structural dependencies into consideration and therefore are used for structured output prediction. In this study, we address the problem of model selection, in the context of CRFs.

Our contribution is twofold. First, we study model selection approaches for discriminative models, in particular for CRFs and propose to penalize the CRFs with the elastic net. Since the penalty term is not differentiable in zero, we consider coordinate-wise optimization. The comparison with the performances of other methods demonstrates competitiveness of the CRFs penalized by the elastic net. Second, we show that the proposed approach is able to take profit of the sparsity to speed up processing and hence potentially handle very large dimensional models.