Cooperative Cuts for Image Segmentation

2010

Technical Report

ei

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.

@techreport{6732,
  title = {Cooperative Cuts for Image Segmentation},
  author = {Jegelka, S. and Bilmes, J.},
  number = {UWEETR-1020-0003},
  organization = {Max-Planck-Gesellschaft},
  institution = {University of Washington, Washington DC, USA},
  school = {Biologische Kybernetik},
  month = aug,
  year = {2010},
  doi = {},
  month_numeric = {8}
}