A genetic algorithm for color image segmentation

Authors:
Alessia Amelio;Clara Pizzuti
Affiliations:
Institute for High Performance Computing and Networking, National Research Council of Italy, CNR-ICAR, Rende, CS, Italy;Institute for High Performance Computing and Networking, National Research Council of Italy, CNR-ICAR, Rende, CS, Italy
Venue:
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
Year:
2013

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Abstract

A genetic algorithm for color image segmentation is proposed. The method represents an image as a weighted undirected graph, where nodes correspond to pixels, and edges connect similar pixels. Similarity between two pixels is computed by taking into account not only brightness, but also color and texture content. Experiments on images from the Berkeley Image Segmentation Dataset show that the method is able to partition natural and human scenes in a number of regions consistent with human visual perception. A quantitative evaluation of the method compared with other approaches shows that the genetic algorithm can be very competitive in partitioning color images.