Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A new approach to the maximum-flow problem
Journal of the ACM (JACM)
A constant factor approximation algorithm for a class of classification problems
STOC '00 Proceedings of the thirty-second annual ACM symposium on Theory of computing
Approximation algorithms
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
Efficient graph-based energy minimization methods in computer vision
Efficient graph-based energy minimization methods in computer vision
Depth Discontinuities by Pixel-to-Pixel Stereo
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Convex Optimization
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Linear Programming Formulation and Approximation Algorithms for the Metric Labeling Problem
SIAM Journal on Discrete Mathematics
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Convergent Tree-Reweighted Message Passing for Energy Minimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Approximate Labeling via Graph Cuts Based on Linear Programming
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the 25th international conference on Machine learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
MAP estimation of semi-metric MRFs via hierarchical graph cuts
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
MAP estimation via agreement on trees: message-passing and linear programming
IEEE Transactions on Information Theory
Exact optimization for Markov random fields with convex priors
IEEE Transactions on Pattern Analysis and Machine Intelligence
The partial constraint satisfaction problem: Facets and lifting theorems
Operations Research Letters
Multi-label Moves for MRFs with Truncated Convex Priors
International Journal of Computer Vision
Joint Optimization for Object Class Segmentation and Dense Stereo Reconstruction
International Journal of Computer Vision
Filter-Based mean-field inference for random fields with higher-order terms and product label-spaces
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
A robust stereo prior for human segmentation
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Inference Methods for CRFs with Co-occurrence Statistics
International Journal of Computer Vision
Computer Vision and Image Understanding
Hi-index | 0.00 |
We consider the problem of obtaining an approximate maximum a posteriori estimate of a discrete random field characterized by pairwise potentials that form a truncated convex model. For this problem, we propose two st-MINCUT based move making algorithms that we call Range Swap and Range Expansion. Our algorithms can be thought of as extensions of αβ-Swap and α-Expansion respectively that fully exploit the form of the pairwise potentials. Specifically, instead of dealing with one or two labels at each iteration, our methods explore a large search space by considering a range of labels (that is, an interval of consecutive labels). Furthermore, we show that Range Expansion provides the same multiplicative bounds as the standard linear programming (LP) relaxation in polynomial time. Compared to previous approaches based on the LP relaxation, for example interior-point algorithms or tree-reweighted message passing (TRW), our methods are faster as they use only the efficient st-MINCUT algorithm in their design. We demonstrate the usefulness of the proposed approaches on both synthetic and standard real data problems.