Perceptual grouping and segmentation by stochastic clustering

Yoram Gdalyahu*, Noam Shental, Daphna Weinshall

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations


We use cluster analysis as a unifying principle for problems from low, middle and high level vision. The clustering problem is viewed as graph partitioning, where nodes represent data elements and the weights of the edges represent pairwise similarities. Our algorithm generates samples of cuts in this graph, by using David Karger's contraction algorithm, and computes an 'average' cut which provides the basis for our solution to the clustering problem. The stochastic nature of our method makes it robust against noise, including accidental edges and small spurious clusters. The complexity of our algorithm is very low: O(N log2 N) for N objects and a fixed accuracy level. Without additional computational cost, our algorithm provides a hierarchy of nested partitions. We demonstrate the superiority of our method for image segmentation on a few real color images. Our second application includes the concatenation of edges in a cluttered scene (perceptual grouping), where we show that the same clustering algorithm achieves as good a grouping, if not better, as more specialized methods.

Original languageAmerican English
Pages (from-to)367-374
Number of pages8
JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
StatePublished - 2000
EventIEEE Conference on Computer Vision and Pattern Recognition, CVPR 2000 - Hilton Head Island, SC, USA
Duration: 13 Jun 200015 Jun 2000


Dive into the research topics of 'Perceptual grouping and segmentation by stochastic clustering'. Together they form a unique fingerprint.

Cite this