Image Segmentation by Nonparametric Clustering Based on the Kolmogorov-Smirnov Distance

Eric J. Pauwels, Greet Frederix

Abstract: In this paper we introduce a non-parametric clustering algorithm for 1-dimensional data. The procedure looks for the simplest (i.e. smoothest) density that is still compatible with the data. Compatibility is given a precise meaning in terms of the Kolmogorov-Smirnov statistic. After discussing experimental results for colour segmentation, we outline how this proposed algorithm can be extended to higher dimensions.