Speeding-up Graphical Model Optimization via a Coarse-to-fine Cascade of Pruning Classifiers
This work addresses computational bottlenecks in graphical model optimization for computer vision, offering a versatile framework that improves efficiency and accuracy.
The paper tackles the problem of slow graphical model optimization by introducing a coarse-to-fine cascade of pruning classifiers, achieving significant time speed-ups and more accurate solutions compared to efficient inference methods in MRF problems.
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.