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Niveau: Supérieur, Doctorat, Bac+8

Reduce, Reuse & Recycle: Efficiently Solving Multi-Label MRFs Karteek Alahari1 Pushmeet Kohli2 Philip H. S. Torr1 1Oxford Brookes University? 2Microsoft Research, Cambridge Abstract In this paper, we present novel techniques that improve the computational and memory efficiency of algorithms for solving multi-label energy functions arising from discrete MRFs or CRFs. These methods are motivated by the ob- servations that the performance of minimization algorithms depends on: (a) the initialization used for the primal and dual variables; and (b) the number of primal variables in- volved in the energy function. Our first method (dynamic ?- expansion) works by ‘recycling' results from previous prob- lem instances. The second method simplifies the energy function by ‘reducing' the number of unknown variables, and can also be used to generate a good initialization for the dynamic ?-expansion algorithm by ‘reusing' dual vari- ables. We test the performance of our methods on energy func- tions encountered in the problems of stereo matching, and colour and object based segmentation. Experimental results show that our methods achieve a substantial improvement in the performance of ?-expansion, as well as other popu- lar algorithms such as sequential tree-reweighted message passing, and max-product belief propagation. In most cases we achieve a 10-15 times speed-up in the computation time. Our modified ?-expansion algorithm provides similar per- formance to Fast-PD [15].

Reduce, Reuse & Recycle: Efficiently Solving Multi-Label MRFs Karteek Alahari1 Pushmeet Kohli2 Philip H. S. Torr1 1Oxford Brookes University? 2Microsoft Research, Cambridge Abstract In this paper, we present novel techniques that improve the computational and memory efficiency of algorithms for solving multi-label energy functions arising from discrete MRFs or CRFs. These methods are motivated by the ob- servations that the performance of minimization algorithms depends on: (a) the initialization used for the primal and dual variables; and (b) the number of primal variables in- volved in the energy function. Our first method (dynamic ?- expansion) works by ‘recycling' results from previous prob- lem instances. The second method simplifies the energy function by ‘reducing' the number of unknown variables, and can also be used to generate a good initialization for the dynamic ?-expansion algorithm by ‘reusing' dual vari- ables. We test the performance of our methods on energy func- tions encountered in the problems of stereo matching, and colour and object based segmentation. Experimental results show that our methods achieve a substantial improvement in the performance of ?-expansion, as well as other popu- lar algorithms such as sequential tree-reweighted message passing, and max-product belief propagation. In most cases we achieve a 10-15 times speed-up in the computation time. Our modified ?-expansion algorithm provides similar per- formance to Fast-PD [15].

- multi-label energy
- algorithm
- using any algorithm
- involves updating
- all ? ?
- problem
- variable
- algorithms
- energy functions

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Publié par | profil-zyak-2012 |

Nombre de lectures | 32 |

Langue | English |

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