Hierarchical distributed genetic algorithm for image segmentation

Proceedings of the 2000 Congress on Evolutionary Computation CEC 00, California, CA, USA, 16-19 July 2000In this paper a new hierarchical distributed genetic algorithm is proposed for image segmentation. Firstly, a technique of histogram dichotomy is proposed to explore the statistical property of input image and produce a hierarchical quantization image. Then a Hierarchical distributed genetic algorithm (HDGA) is imposed on the quantized image to explore the spatial connectivity and produce final segmentation result. HDGA is a major improvement of the original Distributed Genetic Algorithm (DGA) and Multiscale Distributed Genetic Algorithm (MDGA) in four aspects: (1) HDGA does not require the a priori number of image regions, however it can effectively and adaptively controls the segmentation quality; (2) the chromosome structure is revised from the original label (multilabel)-condition-fitness format to a more compact (storage-efficient) label-fitness format; (3) the fitness function is revised to utilized the spatial connectivity, but not the original `reconstruction' error; (4) three revised genetic operations are presented to make the algorithm computation-efficient. Our experiments give proofs for the advantages of HDGA.Department of Electronic and Information Engineerin