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CVPR
1999
IEEE

Stochastic Image Segmentation by Typical Cuts

9 years 11 months ago
Stochastic Image Segmentation by Typical Cuts
We present a stochastic clustering algorithm which uses pairwise similarity of elements, based on a new graph theoretical algorithm for the sampling of cuts in graphs. The stochastic nature of our method makes it robust against noise, including accidental edges and small spurious clusters. We demonstrate the robustness and superiority of our method for image segmentation on a few synthetic examples where other recently proposed methods (such as normalized-cut) fail. In addition, the complexity of our method is lower. We describe experiments with real images showing good segmentation results.
Yoram Gdalyahu, Daphna Weinshall, Michael Werman
Added 12 Oct 2009
Updated 30 Oct 2009
Type Conference
Year 1999
Where CVPR
Authors Yoram Gdalyahu, Daphna Weinshall, Michael Werman
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