International Journal of Computer Vision
Texture Modeling by Multiple Pairwise Pixel Interactions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Filters, Random Fields and Maximum Entropy (FRAME): Towards a Unified Theory for Texture Modeling
International Journal of Computer Vision
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Training products of experts by minimizing contrastive divergence
Neural Computation
A Compact Model for Viewpoint Dependent Texture Synthesis
SMILE '00 Revised Papers from Second European Workshop on 3D Structure from Multiple Images of Large-Scale Environments
Multi-camera Scene Reconstruction via Graph Cuts
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Stereo Matching Using Belief Propagation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparison of Graph Cuts with Belief Propagation for Stereo, using Identical MRF Parameters
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Interactive digital photomontage
ACM SIGGRAPH 2004 Papers
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fields of Experts: A Framework for Learning Image Priors
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Efficient Belief Propagation for Early Vision
International Journal of Computer Vision
Efficient graphical models for processing images
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Texture synthesis via a noncausal nonparametric multiscale Markov random field
IEEE Transactions on Image Processing
Image denoising using scale mixtures of Gaussians in the wavelet domain
IEEE Transactions on Image Processing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
Efficient belief propagation for higher-order cliques using linear constraint nodes
Computer Vision and Image Understanding
International Journal of Computer Vision
Robust Higher Order Potentials for Enforcing Label Consistency
International Journal of Computer Vision
Discriminative word alignment via alignment matrix modeling
StatMT '08 Proceedings of the Third Workshop on Statistical Machine Translation
Learning super resolution with global and local constraints
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Interactive image segmentation using probabilistic hypergraphs
Pattern Recognition
Minimization of monotonically levelable higher order MRF energies via graph cuts
IEEE Transactions on Image Processing
Faster Algorithms for Max-Product Message-Passing
The Journal of Machine Learning Research
Minimizing count-based high order terms in markov random fields
EMMCVPR'11 Proceedings of the 8th international conference on Energy minimization methods in computer vision and pattern recognition
Higher order Markov networks for model estimation
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part I
Computer Vision and Image Understanding
Discrete Applied Mathematics
Tighter relaxations for higher-order models based on generalized roof duality
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
A game-theoretical approach to image segmentation
CVM'12 Proceedings of the First international conference on Computational Visual Media
Window annealing for pixel-labeling problems
Computer Vision and Image Understanding
Computer Vision and Image Understanding
Integrating multiple character proposals for robust scene text extraction
Image and Vision Computing
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Belief propagation (BP) has become widely used for low-level vision problems and various inference techniques have been proposed for loopy graphs. These methods typically rely on ad hoc spatial priors such as the Potts model. In this paper we investigate the use of learned models of image structure, and demonstrate the improvements obtained over previous ad hoc models for the image denoising problem. In particular, we show how both pairwise and higher-order Markov random fields with learned clique potentials capture rich image structures that better represent the properties of natural images. These models are learned using the recently proposed Fields-of-Experts framework. For such models, however, traditional BP is computationally expensive. Consequently we propose some approximation methods that make BP with learned potentials practical. In the case of pairwise models we propose a novel approximation of robust potentials using a finite family of quadratics. In the case of higher order MRFs, with 2× 2 cliques, we use an adaptive state space to handle the increased complexity. Extensive experiments demonstrate the power of learned models, the benefits of higher-order MRFs and the practicality of BP for these problems with the use of simple principled approximations.