We propose a novel framework for graph-based cooperative regularization that uses submodular costs on graph edges. We introduce an efficient iterative algorithm to solve the resulting hard discrete optimization problem, and show that it has a guaranteed approximation factor. The edge-submodular formulation is amenable to the same extensions as standard graph cut approaches, and applicable to a range of problems. We apply this method to the image segmentation problem. Specifically, Here, we apply it to introduce a discount for homogeneous boundaries in binary image segmentation on very difficult images, precisely, long thin objects and color and grayscale images with a shading gradient. The experiments show that significant portions of previously truncated objects are now preserved.
| Author(s): | Jegelka, S. and Bilmes, J. |
| Links: | |
| Number (issue): | UWEETR-1020-0003 |
| Year: | 2010 |
| Month: | August |
| Day: | 0 |
| BibTeX Type: | Technical Report (techreport) |
| Electronic Archiving: | grant_archive |
| Institution: | University of Washington, Washington DC, USA |
| Language: | en |
| Organization: | Max-Planck-Gesellschaft |
| School: | Biologische Kybernetik |
BibTeX
@techreport{6732,
title = {Cooperative Cuts for Image Segmentation},
abstract = {We propose a novel framework for graph-based cooperative regularization that uses submodular costs on graph edges. We introduce an efficient iterative algorithm to solve the resulting hard discrete optimization problem, and show that it has a guaranteed approximation factor. The edge-submodular formulation is amenable to the same extensions as standard graph cut approaches, and applicable to a range of problems. We apply this method to the image segmentation problem. Specifically, Here, we apply it to introduce a discount for homogeneous boundaries in binary image segmentation on very difficult images, precisely, long thin objects and color and grayscale images with a shading gradient. The experiments show that significant portions of previously truncated objects are now preserved.},
number = {UWEETR-1020-0003},
organization = {Max-Planck-Gesellschaft},
institution = {University of Washington, Washington DC, USA},
school = {Biologische Kybernetik},
month = aug,
year = {2010},
author = {Jegelka, S. and Bilmes, J.},
month_numeric = {8}
}