Figure-ground separation: A case study in energy minimization via evolutionary computing

Abstract

It is known that the problem of figure-ground separation can be modeled as one of energy minimization using the Ising system model from quantum physics. The Ising system model for the figure-ground separation problem makes explicit the definition of shape in terms of attributes such as cocircularity, smoothness, proximity and contrast and is based on the formulation of an energy function that incorporates pairwise interactions between local image features in the form of edgels. This paper explores a class of stochastic optimization techniques based on evolutionary algorithms for the problem of figure-ground separation using the Ising system model. Experimental results on synthetic edgel maps and edgel maps derived from gray scale images are presented. The advantages and shortcomings of evolutionary algorithms in the context of figure-ground separation are discussed.