Combining boundary and region features inside the combinatorial pyramid for topology-preserving perceptual image segmentation

Abstract

Combinatorial pyramids represent the image as a stack of successively reduced combi- natorial maps, which encode the whole image at different levels of abstraction. Within this framework, this paper proposes to conduct the perceptual organization of the im- age content in two consecutive stages. The first stage builds the lower set of levels of the hierarchy according to simple face (regions) features (colour and size). On the top of this hierarchy, the second stage will mainly employ boundary features, encoded in the darts of the combinatorial maps, to obtain a second set of levels of abstraction. The Berkeley data set BSDS300 is used to quantitatively compare the performance of the proposal to a number of perceptual grouping approaches, showing that it yields better or similar results than most of these algorithms while offering two interesting features: computation at multiple image resolutions and preservation of the image topology.