Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discrete Applied Mathematics
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning structured prediction models: a large margin approach
ICML '05 Proceedings of the 22nd international conference on Machine learning
Recovering Surface Layout from an Image
International Journal of Computer Vision
Training structural SVMs when exact inference is intractable
Proceedings of the 25th international conference on Machine learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning CRFs Using Graph Cuts
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
International Journal of Computer Vision
Robust Higher Order Potentials for Enforcing Label Consistency
International Journal of Computer Vision
Multiscale conditional random fields for image labeling
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Making the right moves: Guiding alpha-expansion using local primal-dual gaps
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Efficient training for pairwise or higher order CRFs via dual decomposition
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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Pairwise Markov random fields are an effective framework for solving many pixel labeling problems in computer vision. However, their performance is limited by their inability to capture higher-order correlations. Recently proposed higher-order models are showing superior performance to their pairwise counterparts. In this paper, we derive two variants of the higher-order lower linear envelop model and show how to perform tractable move-making inference in these models. We propose a novel use of this model for encoding consistency constraints over large sets of pixels. Importantly these pixel sets do not need to be contiguous. However, the consistency model has a large number of parameters to be tuned for good performance. We exploit the structured SVM paradigm to learn optimal parameters and show some practical techniques to overcome huge computation requirements. We evaluate our model on the problems of image denoising and semantic segmentation.