Stochastic image segmentation by typical cuts

Yoram Gdalyahu*, Daphna Weinshall, Michael Werman

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

34 Scopus citations

Abstract

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.

Original languageAmerican English
Pages (from-to)596-601
Number of pages6
JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume2
StatePublished - 1999
EventProceedings of the 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'99) - Fort Collins, CO, USA
Duration: 23 Jun 199925 Jun 1999

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