Algorithm that mimics human perceptual grouping of dot patterns

We propose an algorithm that groups points similarly to how human observers do. It is simple, totally unsupervised and able to find clusters of complex and not necessarily convex shape. Groups are identified as the connected components of a Reduced Delaunay Graph (RDG) that we define in this paper. Our method can be seen as an algorithmic equivalent of the gestalt law of perceptual grouping according to proximity. We introduce a measure of dissimilarity between two different groupings of a point set and use this measure to compare our algorithm with human visual perception and the kappa-means clustering algorithm. Our algorithm mimics human perceptual grouping and outperforms the k-means algorithm in all cases that we studied. We also sketch a potential application in the segmentation of structural textures.