Learnable Swendsen-Wang Cuts for Image Segmentation

We propose a framework for Bayesian unsupervised image segmentation with descriptive, learnable models. Our approach is based on learning descriptive models for segmentation and applying Monte Carlo Markov chain to traverse the solution space. Swendsen-Wang cuts are adapted to make meaningful jumps in solution space.