  While widely acknowledged as highly effective in computer vision, multi-label
MRFs with non-convex priors are difficult to optimize. To tackle this, we
introduce an algorithm that iteratively approximates the original energy with
an appropriately weighted surrogate energy that is easier to minimize. Our
algorithm guarantees that the original energy decreases at each iteration. In
particular, we consider the scenario where the global minimizer of the weighted
surrogate energy can be obtained by a multi-label graph cut algorithm, and show
that our algorithm then lets us handle of large variety of non-convex priors.
We demonstrate the benefits of our method over state-of-the-art MRF energy
minimization techniques on stereo and inpainting problems.
