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Genetic-Based Multiresolution Color Image Segmentation

The goal of image segmentation is to partition an image into some homogeneous regions. In this paper, a novel neighborhood-based segmentation approach is proposed. Genetic algorithm is used in the proposed twopass process. In the first pass, an energy function, which is defined based on Markov Random Fields, is minimized. In the second pass, which is optional, segmentation results obtained from the first pass with different values of a parameter are used as initial population and are optimized with respect to a given evaluation criterion. Different evaluation criteria can be used in our algorithm and a new criterion is also proposed. A quadtree is employed to implement the multiresolution framework, which enables the use of different strategies at different resolution levels, and hence, the computation can be accelerated. In addition, a new encoding mechanism for the quadtree structure is proposed. New crossover and mutation operators, namely graft crossover, splitting, merging, and alteration mutation, which are suitable for the quadtree structure, are introduced. These operators guarantee that the resulting tree is a quadtree. The experimental results using the proposed segmentation approach are very encouraging.