Unsupervised image segmentation using a distributed genetic algorithm

A new methodological approach to digital image processing applied to the particular case of gray-level image segmentation is introduced. The method is based on a modified and simplified version of classifier systems. The labeling function is implemented as a spatially structured set of binary-coded production rules. The labeling is iteratively modified using a distributed genetic algorithm. Results are presented which illustrate both the mechanisms underlying the functioning of the method and its performance on natural images. The relationships between this approach and other related techniques are discussed and it is shown that it compares favorably with these.