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 in short as 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 .
|Number of pages||9|
|Journal||Advances in Neural Information Processing Systems|
|Publication status||Published (in print/issue) - 1 Jan 2014|
|Event||28th Annual Conference on Neural Information Processing Systems 2014, NIPS 2014 - Montreal, Canada|
Duration: 8 Dec 2014 → 13 Dec 2014