Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
The robust estimation of multiple motions: parametric and piecewise-smooth flow fields
Computer Vision and Image Understanding
A constant factor approximation algorithm for a class of classification problems
STOC '00 Proceedings of the thirty-second annual ACM symposium on Theory of computing
Approximation algorithms for the metric labeling problem via a new linear programming formulation
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
Approximation algorithms
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Multi-camera Scene Reconstruction via Graph Cuts
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Segmentation by Grouping Junctions
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Efficient graph-based energy minimization methods in computer vision
Efficient graph-based energy minimization methods in computer vision
A Maximum-Flow Formulation of the N-Camera Stereo Correspondence Problem
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
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
Approximate classification via earthmover metrics
SODA '04 Proceedings of the fifteenth annual ACM-SIAM symposium on Discrete algorithms
Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods
International Journal of Computer Vision
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
A New Framework for Approximate Labeling via Graph Cuts
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Spatially coherent clustering using graph cuts
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Editorial: Discrete optimization in computer vision
Computer Vision and Image Understanding
Computer Vision and Image Understanding
MAP-Inference for Highly-Connected Graphs with DC-Programming
Proceedings of the 30th DAGM symposium on Pattern Recognition
Iterated conditional modes for inverse dithering
Signal Processing
A Study of Parts-Based Object Class Detection Using Complete Graphs
International Journal of Computer Vision
Generalized sparse MRF appearance models
Image and Vision Computing
Expression mimicking: from 2D monocular sequences to 3D animations
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
Optimizing complex loss functions in structured prediction
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
A continuous max-flow approach to potts model
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Fast multi-labelling for stereo matching
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Challenges of medical image processing
Computer Science - Research and Development
Collective Inference for Extraction MRFs Coupled with Symmetric Clique Potentials
The Journal of Machine Learning Research
Improved Moves for Truncated Convex Models
The Journal of Machine Learning Research
Global Minimization for Continuous Multiphase Partitioning Problems Using a Dual Approach
International Journal of Computer Vision
Metric labeling and semi-metric embedding for protein annotation prediction
RECOMB'11 Proceedings of the 15th Annual international conference on Research in computational molecular biology
Semantic colorization with internet images
Proceedings of the 2011 SIGGRAPH Asia Conference
A Spatial Regularization Approach for Vector Quantization
Journal of Mathematical Imaging and Vision
Parallel and distributed vision algorithms using dual decomposition
Computer Vision and Image Understanding
Semantic classification in aerial imagery by integrating appearance and height information
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
Conditional random fields and supervised learning in automated skin lesion diagnosis
Journal of Biomedical Imaging - Special issue on Machine Learning in Medical Imaging
Continuous Multiclass Labeling Approaches and Algorithms
SIAM Journal on Imaging Sciences
Motion Coherent Tracking Using Multi-label MRF Optimization
International Journal of Computer Vision
Generic cuts: an efficient algorithm for optimal inference in higher order MRF-MAP
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Review article: Multilabel partition moves for MRF optimization
Image and Vision Computing
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
Computer Vision and Image Understanding
Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem
Journal of Mathematical Imaging and Vision
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A new framework is presented for both understanding and developing graph-cut-based combinatorial algorithms suitable for the approximate optimization of a very wide class of Markov Random Fields (MRFs) that are frequently encountered in computer vision. The proposed framework utilizes tools from the duality theory of linear programming in order to provide an alternative and more general view of state-of-the-art techniques like the \alpha-expansion algorithm, which is included merely as a special case. Moreover, contrary to \alpha-expansion, the derived algorithms generate solutions with guaranteed optimality properties for a much wider class of problems, for example, even for MRFs with nonmetric potentials. In addition, they are capable of providing per-instance suboptimality bounds in all occasions, including discrete MRFs with an arbitrary potential function. These bounds prove to be very tight in practice (that is, very close to 1), which means that the resulting solutions are almost optimal. Our algorithms' effectiveness is demonstrated by presenting experimental results on a variety of low-level vision tasks, such as stereo matching, image restoration, image completion, and optical flow estimation, as well as on synthetic problems.