Higher-Order Energies for Image Segmentation.

A novel energy minimization method for general higher-order binary energy functions is proposed in this paper. We first relax a discrete higher-order function to a continuous one, and use the Taylor expansion to obtain an approximate lower-order function, which is optimized by the quadratic pseudo-boolean optimization (QPBO) or other discrete optimizers. The minimum solution of this lower-order function is then used as a new local point, where we expand the original higher-order energy function again. Our algorithm does not restrict to any specific form of the higher-order binary function or bring in extra auxiliary variables. For concreteness, we show an application of segmentation with the appearance entropy, which is efficiently solved by our method. Experimental results demonstrate that our method outperforms state-of-the-art methods.