Genetic Algorithms: Colour Image Segmentation Literature Review
Keri J. Woods · 2007
Image segmentation has great importance in many image processing applications, and yet no general image segmentation exists. Image segmentation is complicated task, often with many parameters needing to be tuned to get good results. This report researches and discusses the concepts of image segmentation, genetic algo-rithms and segmentation evaluation. Due to the flexibility of genetic algorithms and their ability to effectively explore large search spaces, it may be viable to use them to improve existing image segmentation methods. A region merging algorithm was implemented and evaluated using quantitative means. These segmentation results were compared to those of two other image segmentation methods: region growing and watershed segmentation. Our region merging method was shown to produce average results. A genetic algorithm was implemented in an attempt to improve the segmentation results by evolving the segmentation parameters. A fitness func-tion needing neither human input nor a ground truth segmentation comparison was proposed. The results on the effect of the genetic algorithm on the performance of