An efficient algorithm for finding the M most probable configurationsin probabilistic expert systems
Statistics and Computing
What Energy Functions Can Be Minimized via Graph Cuts?
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Discrete Applied Mathematics
Markov Random Fields with Efficient Approximations
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Energy Minimization via Graph Cuts: Settling What is Possible
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Effciently Solving Dynamic Markov Random Fields Using Graph Cuts
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Convergent Tree-Reweighted Message Passing for Energy Minimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic Graph Cuts for Efficient Inference in Markov Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Registration with Uncertainties and Statistical Modeling of Shapes with Variable Metric Kernels
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exploiting inference for approximate parameter learning in discriminative fields: an empirical study
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
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
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
On the optimality of solutions of the max-product belief-propagation algorithm in arbitrary graphs
IEEE Transactions on Information Theory
Exact optimization for Markov random fields with convex priors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimizing complex loss functions in structured prediction
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Interactively Co-segmentating Topically Related Images with Intelligent Scribble Guidance
International Journal of Computer Vision
Diverse M-best solutions in markov random fields
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Probexplorer: uncertainty-guided exploration and editing of probabilistic medical image segmentation
EuroVis'10 Proceedings of the 12th Eurographics / IEEE - VGTC conference on Visualization
Computer Vision and Image Understanding
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In recent years graph cuts have become a popular tool for performing inference in Markov and conditional random fields. In this context the question arises as to whether it might be possible to compute a measure of uncertainty associated with the graph cut solutions. In this paper we answer this particular question by showing how the min-marginals associated with the label assignments of a random field can be efficiently computed using a new algorithm based on dynamic graph cuts. The min-marginal energies obtained by our proposed algorithm are exact, as opposed to the ones obtained from other inference algorithms like loopy belief propagation and generalized belief propagation. The paper also shows how min-marginals can be used for parameter learning in conditional random fields.