Name
Abstract | ||
|---|---|---|
We present a stochastic clustering algorithm which uses pairwise similarity of elements, based on a new graph the- oretical 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 re- sults. |
Year | DOI | Venue |
|---|---|---|
1999 | 10.1109/CVPR.1999.784979 | Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference. |
Keywords | Field | DocType |
computational complexity,graph theory,image segmentation,stochastic processes,accidental edges,complexity,graph theoretical algorithm,pairwise similarity,spurious clusters,stochastic clustering algorithm,stochastic image segmentation,typical cuts | Graph theory,Scale-space segmentation,Pattern recognition,Computer science,Segmentation,Segmentation-based object categorization,Robustness (computer science),Image segmentation,Artificial intelligence,Real image,Cluster analysis | Conference |
Volume | Issue | ISSN |
2 | 1 | 1063-6919 |
ISBN | Citations | PageRank |
0-7695-0149-4 | 23 | 7.04 |
References | Authors | |
6 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yoram Gdalyahu | 1 | 324 | 68.58 |
| Daphna Weinshall | 2 | 1780 | 273.93 |
| M. Werman | 3 | 343 | 112.04 |