Image segmentation using a genetic algorithm and hierarchical local search

Authors:
Mark Hauschild;Sanjiv Bhatia;Martin Pelikan
Affiliations:
University of Missouri-St. Louis, St. Louis, MO, USA;University of Missouri-St. Louis, St. Louis, MO, USA;University of Missouri-St. Louis, St. Louis, MO, USA
Venue:
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Year:
2012

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Abstract

This paper proposes a hybrid genetic algorithm to perform image segmentation based on applying the q-state Potts spin glass model to a grayscale image. First, the image is converted to a set of weights for a q-state spin glass and then a steady-state genetic algorithm is used to evolve candidate segmented images until a suitable candidate solution is found. To speed up the convergence to an adequate solution, hierarchical local search is used on each evaluated solution. The results show that the hybrid genetic algorithm with hierarchical local search is able to efficiently perform image segmentation. The necessity of hierarchical search for these types of problems is also clearly demonstrated.