DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
A continuous max-flow approach to potts model
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Proceedings of the 32nd DAGM conference on Pattern recognition
Global Minimization for Continuous Multiphase Partitioning Problems Using a Dual Approach
International Journal of Computer Vision
Computer Vision and Image Understanding
International Journal of Computer Vision
Minimizing a sum of submodular functions
Discrete Applied Mathematics
Discrete Applied Mathematics
Tighter relaxations for higher-order models based on generalized roof duality
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
Submodular relaxation for MRFs with high-order potentials
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
Computer Vision and Image Understanding
| Hi-index | 0.14 |
In this paper, we extend the class of energy functions for which the optimal \alpha-expansion and \alpha \beta-swap moves can be computed in polynomial time. Specifically, we introduce a novel family of higher order clique potentials, and show that the expansion and swap moves for any energy function composed of these potentials can be found by minimizing a submodular function. We also show that for a subset of these potentials, the optimal move can be found by solving an st-mincut problem. We refer to this subset as the {\cal P}^n Potts model. Our results enable the use of powerful \alpha-expansion and \alpha \beta-swap move making algorithms for minimization of energy functions involving higher order cliques. Such functions have the capability of modeling the rich statistics of natural scenes and can be used for many applications in Computer Vision. We demonstrate their use in one such application, i.e., the texture-based image or video-segmentation problem.