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arXiv · 1409.4205

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

B. Conejo·N. Komodakis·S. Leprince·J. P. Avouac
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Abstract

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.

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B. Conejo, N. Komodakis, S. Leprince, J. P. Avouac. 2014-09-15. Speeding-up Graphical Model Optimization via a Coarse-to-fine Cascade of Pruning Classifiers. https://arxiv.org/abs/1409.4205

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