Learning to approximate global shape priors for figure-ground segmentation
Daniel Kuettel, Vittorio Ferrari · 2013
We present a technique for approximate minimization of two-label energy functions with higher-order or global potentials. Our method treats the energy function as a blackbox: it does not exploit knowledge of its form nor its order, as opposed to optimization schemes specialized to a particular form. The key idea is to automatically learn a lower-order approximation of the energy function, which can then be minimized used existing efficient algorithms. We experimentally demonstrate our method for binary image segmentation, where it enables to incorporate a global shape prior into traditional models based on pairwise conditional random fields.