  We propose a general and versatile framework that significantly speeds-up
graphical model optimization while maintaining an excellent solution accuracy.
The proposed approach relies on a multi-scale pruning scheme that is able to
progressively reduce the solution space by use of a novel strategy based on a
coarse-to-fine cascade of learnt classifiers. We thoroughly experiment with
classic computer vision related MRF problems, where our framework constantly
yields a significant time speed-up (with respect to the most efficient
inference methods) and obtains a more accurate solution than directly
optimizing the MRF.
