Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
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
Occlusions, Discontinuities, and Epipolar Lines in Stereo
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
What Energy Functions Can Be Minimized via Graph Cuts?
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Multi-camera Scene Reconstruction via Graph Cuts
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Markov Random Fields with Efficient Approximations
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Graphcut textures: image and video synthesis using graph cuts
ACM SIGGRAPH 2003 Papers
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
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Photographing long scenes with multi-viewpoint panoramas
ACM SIGGRAPH 2006 Papers
Cosegmentation of Image Pairs by Histogram Matching - Incorporating a Global Constraint into MRFs
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Probabilistic Fusion of Stereo with Color and Contrast for Bilayer Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Convergent Tree-Reweighted Message Passing for Energy Minimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scene completion using millions of photographs
ACM SIGGRAPH 2007 papers
Image and depth from a conventional camera with a coded aperture
ACM SIGGRAPH 2007 papers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-label Moves for MRFs with Truncated Convex Priors
EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Solving Multilabel Graph Cut Problems with Multilabel Swap
DICTA '09 Proceedings of the 2009 Digital Image Computing: Techniques and Applications
Improved Moves for Truncated Convex Models
The Journal of Machine Learning Research
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
High-accuracy stereo depth maps using structured light
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Comparison of energy minimization algorithms for highly connected graphs
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Exact optimization for Markov random fields with convex priors
IEEE Transactions on Pattern Analysis and Machine Intelligence
New algorithms for convex cost tension problem with application to computer vision
Discrete Optimization
Computer Vision and Image Understanding
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Optimization with graph cuts became very popular in recent years. While exact optimization is possible in a few cases, many useful energy functions are NP hard to optimize. One approach to approximate optimization is the so-called move making algorithms. At each iteration, a move-making algorithm makes a proposal (move) for a pixel p to either keep its old label or switch to a new label. Two move-making algorithms based on graph cuts are in wide use, namely the swap and expansion. Both of these moves are binary in nature, that is they give each pixel a choice of only two labels. An evaluation of optimization techniques shows that the expansion and swap algorithms perform very well for energies where the underlying MRF has the Potts prior. However for more general priors, the swap and expansion algorithms do not perform as well. The main contribution of this paper is to develop multi-label moves. A multi-label move, unlike expansion and swap, gives each pixel has a choice of more than two labels to switch to. In particular, we develop several multi-label moves for truncated convex priors. We evaluate our moves on image restoration, inpainting, and stereo correspondence. We get better results than expansion and swap algorithms, both in terms of the energy value and accuracy.