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 log 2 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.