Stochastic image segmentation by typical cuts
- 20 January 2003
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 2 (10636919) , 596-601
- https://doi.org/10.1109/cvpr.1999.784979
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.Keywords
This publication has 4 references indexed in Scilit:
- Data Clustering Using a Model Granular MagnetNeural Computation, 1997
- Pairwise data clustering by deterministic annealingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1997
- A new approach to the minimum cut problemJournal of the ACM, 1996
- An optimal graph theoretic approach to data clustering: theory and its application to image segmentationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1993