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

Speeding-up Graphical Model Optimization via a Coarse-to-fine Cascade of Pruning Classifiers