@inproceedings{lempitsky2008image,
author = {Lempitsky, Victor and Blake, Andrew and Rother, Carsten},
title = {Image Segmentation by Branch-and-Mincut},
booktitle = {10th European Conference on Computer Vision - ECCV (4)},
year = {2008},
month = {January},
abstract = {Efficient global optimization techniques such as graph cut exist for energies corresponding to binary image segmentation from low-level cues. However, introducing a high-level prior such as a shape prior or a color-distribution prior into the segmentation process typically results in an energy that is much harder to optimize. The main contribution of the paper is a new global optimization framework for a wide class of such energies. The framework is built upon two powerful techniques: graph cut and branch-and-bound. These techniques are unified through the derivation of lower bounds on the energies. Being computable via graph cut, these bounds are used to prune branches within a branch-and-bound search. We demonstrate that the new framework can compute globally optimal segmentations for a variety of segmentation scenarios in a reasonable time on a modern CPU. These scenarios include unsupervised segmentation of an object undergoing 3D pose change, category-specific shape segmentation, and the segmentation under intensity/color priors denfined by Chan-Vese and GrabCut functionals.},
publisher = {Springer Verlag},
url = {http://approjects.co.za/?big=en-us/research/publication/image-segmentation-by-branch-and-mincut/},
pages = {15-29},
edition = {10th European Conference on Computer Vision - ECCV (4)},
}