A revolution: belief propagation in graphs with cycles
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
An introduction to variational methods for graphical models
Learning in graphical models
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
Expectation Propagation for approximate Bayesian inference
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Proceedings of the thirty-seventh annual ACM symposium on Theory of computing
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
A Linear Programming Approach to Max-Sum Problem: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical priors for efficient combinatorial optimization via graph cuts
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
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We consider image and signal segmentation problems within the Markov random field (MRF) approach and try to take into account label frequency constraints. Incorporating these constraints into MRF leads to an NP-hard optimization problem. For solving this problem we present a two-step approximation scheme that allows one to use hard, interval and soft constraints on label frequencies. On the first step a factorized approximation of the joint distribution is made (only local terms are included) and then, on the second step, the labeling is found by conditional maximization of the factorized joint distribution. The latter task is reduced to an easy-to-solve transportation problem. Basing on the proposed two-step approximation scheme we derive the ELM algorithm for tuning MRF parameters. We show the efficiency of our approach on toy signals and on the task of automated segmentation of Google Maps.