Name
Title | ||
|---|---|---|
Speeding-up Graphical Model Optimization via a Coarse-to-fine Cascade of Pruning Classifiers. |
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. |
Year | Venue | Field |
|---|---|---|
2014 | CoRR | Data mining,Pattern recognition,Computer science,Inference,Artificial intelligence,Cascade,Graphical model,Machine learning,Pruning |
DocType | Volume | Citations |
Journal | abs/1409.4205 | 1 |
PageRank | References | Authors |
0.35 | 24 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bruno Conejo | 1 | 1 | 0.35 |
| Nikos Komodakis | 2 | 1 | 1.37 |
| Sébastien Leprince | 3 | 1 | 0.35 |
| Jean-Philippe Avouac | 4 | 64 | 8.59 |