Journal of Optimization Theory and Applications
Quadratic programming relaxations for metric labeling and Markov random field MAP estimation
ICML '06 Proceedings of the 23rd international conference on Machine learning
Solving Markov Random Fields using Second Order Cone Programming Relaxations
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Convergent Tree-Reweighted Message Passing for Energy Minimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Linear Programming Approach to Max-Sum Problem: A Review
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
Learning of graphical models and efficient inference for object class recognition
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Comparison of energy minimization algorithms for highly connected graphs
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
A comparative study of energy minimization methods for markov random fields
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Constructing free-energy approximations and generalized belief propagation algorithms
IEEE Transactions on Information Theory
MAP estimation via agreement on trees: message-passing and linear programming
IEEE Transactions on Information Theory
| Hi-index | 0.00 |
The design of inference algorithms for discrete-valued Markov Random Fields constitutes an ongoing research topic in computer vision. Large state-spaces, none-submodular energy-functions, and highly-connected structures of the underlying graph render this problem particularly difficult. Established techniques that work well for sparsely connected grid-graphs used for image labeling, degrade for non-sparse models used for object recognition.In this context, we present a new class of mathematically sound algorithms that can be flexibly applied to this problem class with a guarantee to converge to a critical point of the objective function. The resulting iterative algorithms can be interpreted as simple message passing algorithms that converge by construction, in contrast to other message passing algorithms.Numerical experiments demonstrate its performance in comparison with established techniques.