Ant colony optimization for the K-means algorithm in image segmentation

Authors:
Chih-Cheng Hung;Mojia Sun
Affiliations:
Southern Polytechnic State University, Marietta, GA;Southern Polytechnic State University, Marietta, GA
Venue:
Proceedings of the 48th Annual Southeast Regional Conference
Year:
2010

Citing 4
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Abstract

In this paper the ant colony optimization (ACO) is used in the K-means algorithm for improving the image segmentation. The learning mechanism of this algorithm is formulated by using the ACO meta-heuristic. As the pheromone dominates the exploration of ants for problem solutions, preliminary experiments on pheromone's update are reported. Two methods for defining and updating pheromone values are proposed and tested: one with the spatial coordinate distances and the other without using such a distance. The ACO improves the K-means algorithm by making it less dependent on the initial parameters.