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  Cooperative Cuts for Image Segmentation

Jegelka, S., & Bilmes, J.(2010). Cooperative Cuts for Image Segmentation (UWEETR-1020-0003). Seattle, WA, USA: Department of Electrical Engineering, University of Washington.

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Jegelka, S1, 2, Author           
Bilmes, J, Author           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 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.

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 Dates: 2010-08
 Publication Status: Published in print
 Pages: 21
 Publishing info: Seattle, WA, USA : Department of Electrical Engineering, University of Washington
 Table of Contents: -
 Rev. Type: -
 Identifiers: Report Nr.: UWEETR-1020-0003
BibTex Citekey: 6732
 Degree: -

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Title: UWEE Technical Report
Source Genre: Series
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Pages: - Volume / Issue: UWEETR-1020-0003 Sequence Number: - Start / End Page: 1 - 21 Identifier: -