Previous work develops efficient approximate optimization based on mean field inference, which is a local optimization method and can be far from the optimum.
As shown in the paper, for many forms of convexity our regularization model is significantly more descriptive for any given k. Our shape prior is useful in practice, e. g. in biomedical applications, and its optimization is robust to local minima.
First, unlike LSA-AUX which selects auxiliary functions based solely on the current solution, we propose to incorporate several additional criteria.
We propose an effective optimization algorithm for a general hierarchical segmentation model with geometric interactions between segments.
Although this is not as good as the factor of 2 approximation of the well known expansion algorithm, we achieve very good results in practice.