Evolutionary image segmentation based on multiobjective clustering

In the fields of image processing and recognition, image segmentation is an important basic technique in which an image is partitioned into multiple regions (sets of pixels). In this paper, we propose a method for evolutionary image segmentation based on multiobjective clustering. In this method, two objectives, overall deviation and edge value, are optimized simultaneously using a multiobjective evolutionary algorithm. These objectives are important factors for image segmentation. The proposed method finds various solutions (image segmentation results) by the use of an evolutionary process. We apply the proposed method to several image segmentation problems and confirm that various solutions are obtained. In addition, we use a simple heuristic method to select one solution from the original Pareto solutions and show that a good image segmentation result is selected.

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