Minimization methods for non-differentiable functions
Minimization methods for non-differentiable functions
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Segmentation by Data-Driven Markov Chain Monte Carlo
IEEE Transactions on Pattern Analysis and Machine Intelligence
An efficient algorithm for finding the M most probable configurationsin probabilistic expert systems
Statistics and Computing
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Convex Optimization
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
Generalizing Swendsen-Wang to Sampling Arbitrary Posterior Probabilities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Effciently Solving Dynamic Markov Random Fields Using Graph Cuts
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
A Linear Programming Approach to Max-Sum Problem: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Predicting diverse subsets using structural SVMs
Proceedings of the 25th international conference on Machine learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Measuring uncertainty in graph cut solutions
Computer Vision and Image Understanding
International Journal of Computer Vision
Graph cut based inference with co-occurrence statistics
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
C^4: Exploring Multiple Solutions in Graphical Models by Cluster Sampling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Branch and bound strategies for non-maximal suppression in object detection
EMMCVPR'11 Proceedings of the 8th international conference on Energy minimization methods in computer vision and pattern recognition
Articulated pose estimation with flexible mixtures-of-parts
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
N-best maximal decoders for part models
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Perturb-and-MAP random fields: Using discrete optimization to learn and sample from energy models
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Bucket and mini-bucket schemes for m best solutions over graphical models
GKR'11 Proceedings of the Second international conference on Graph Structures for Knowledge Representation and Reasoning
Beyond feature points: structured prediction for monocular non-rigid 3d reconstruction
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
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Much effort has been directed at algorithms for obtaining the highest probability (MAP) configuration in probabilistic (random field) models. In many situations, one could benefit from additional high-probability solutions. Current methods for computing the M most probable configurations produce solutions that tend to be very similar to the MAP solution and each other. This is often an undesirable property. In this paper we propose an algorithm for the Diverse M-Best problem, which involves finding a diverse set of highly probable solutions under a discrete probabilistic model. Given a dissimilarity function measuring closeness of two solutions, our formulation involves maximizing a linear combination of the probability and dissimilarity to previous solutions. Our formulation generalizes the M-Best MAP problem and we show that for certain families of dissimilarity functions we can guarantee that these solutions can be found as easily as the MAP solution.