Multiscale annealing for grouping and unsupervised texture segmentation
Computer Vision and Image Understanding
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computing Geodesics and Minimal Surfaces via Graph Cuts
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
"GrabCut": interactive foreground extraction using iterated graph cuts
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
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Effciently Solving Dynamic Markov Random Fields Using Graph Cuts
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Particle Video: Long-Range Motion Estimation using Point Trajectories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Graph Cuts via $\ell_1$ Norm Minimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
TurboPixels: Fast Superpixels Using Geometric Flows
IEEE Transactions on Pattern Analysis and Machine Intelligence
Towards more efficient and effective LP-based algorithms for MRF optimization
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Superpixels and supervoxels in an energy optimization framework
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Uncertainty driven multi-scale optimization
Proceedings of the 32nd DAGM conference on Pattern recognition
SlimCuts: graphcuts for high resolution images using graph reduction
EMMCVPR'11 Proceedings of the 8th international conference on Energy minimization methods in computer vision and pattern recognition
Accurate banded graph cut segmentation of thin structures using laplacian pyramids
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Variable grouping for energy minimization
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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In this paper we propose an algorithm for image segmentation using graph cuts which can be used to efficiently solve labeling problems on high resolution images or image sequences. The basic idea of our method is to group large homogeneous regions to one single variable. Therefore we combine the appearance and the task specific similarity with Dempster's theory of evidence to compute the basic belief that two pixels/groups will have the same label in the minimum energy state. Experiments on image and video segmentation show that our grouping leads to a significant speedup and memory reduction of the labeling problem. Thus large-scale labeling problems can be solved in an efficient manner with a low approximation loss.