Multiscale information fusion by graph cut through convex optimization
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part III
A convex image segmentation: extending graph cuts and closed-form matting
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
Global relabeling for continuous optimization in binary image segmentation
EMMCVPR'11 Proceedings of the 8th international conference on Energy minimization methods in computer vision and pattern recognition
Curvature regularity for multi-label problems - standard and customized linear programming
EMMCVPR'11 Proceedings of the 8th international conference on Energy minimization methods in computer vision and 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
Parallel and distributed vision algorithms using dual decomposition
Computer Vision and Image Understanding
Combinatorial Continuous Maximum Flow
SIAM Journal on Imaging Sciences
Efficient pixel-grouping based on dempster's theory of evidence for image segmentation
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
| Hi-index | 0.14 |
Graph cuts have become an increasingly important tool for solving a number of energy minimization problems in computer vision and other fields. In this paper, the graph cut problem is reformulated as an unconstrained $\ell_1$ norm minimization which can be solved effectively using interior point methods. This reformulation exposes connections between the graph cuts and other related continuous optimization problems. Eventually the problem is reduced to solving a sequence of sparse linear systems involving the Laplacian of the underlying graph. The proposed procedure exploits the structure of these linear systems in a manner that is easily amenable to parallel implementations. Experimental results obtained by applying the procedure to graphs derived from image processing problems are provided.