Region competition: unifying snakes, region growing, and Bayes/MDL for multiband image segmentation

TLDR

The authors introduce a novel statistical and variational image‑segmentation method called region competition. Region competition is derived by minimizing a generalized Bayes/MDL criterion via a variational principle, guaranteeing convergence to a local minimum while integrating snakes/balloons and region‑growing concepts, and is analytically characterized for boundary accuracy, initialization, and edge‑detection relationships, with extensions to multiband gray, color, and texture images. The method subsumes classic snakes/balloons and region‑growing algorithms, generalizes to multiband segmentation, and its color model removes intensity gradients and shadows to segment based on albedo while also detecting highlight regions.

Abstract

We present a novel statistical and variational approach to image segmentation based on a new algorithm, named region competition. This algorithm is derived by minimizing a generalized Bayes/minimum description length (MDL) criterion using the variational principle. The algorithm is guaranteed to converge to a local minimum and combines aspects of snakes/balloons and region growing. The classic snakes/balloons and region growing algorithms can be directly derived from our approach. We provide theoretical analysis of region competition including accuracy of boundary location, criteria for initial conditions, and the relationship to edge detection using filters. It is straightforward to generalize the algorithm to multiband segmentation and we demonstrate it on gray level images, color images and texture images. The novel color model allows us to eliminate intensity gradients and shadows, thereby obtaining segmentation based on the albedos of objects. It also helps detect highlight regions.