We propose a general and versatile framework that significantly speeds-up graphical model optimization while maintaining an excellent solution accuracy. The proposed approach, refereed as Inference by Learning or IbyL, relies on a multi-scale pruning scheme that progressively reduces the solution space by use of a coarse-to-fine cascade of learnt classifiers. We thoroughly experiment with classic computer vision related MRF problems, where our novel 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. We make our code available on-line.
Bruno Conejo (Ecole des Ponts ParisTech)
Nikos Komodakis (Ecole des Ponts ParisTech)
Sebastien Leprince (Caltech)
Jean Philippe Avouac (Caltech)
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