Using ant colony optimization and self-organizing map for image segmentation

Authors:
Sara Saatchi;Chih-Cheng Hung
Affiliations:
School of Computing and Software Engineering, Southern Polytechnic State University, Marietta, GA;School of Computing and Software Engineering, Southern Polytechnic State University, Marietta, GA
Venue:
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Year:
2007

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Abstract

In this study, ant colony optimization (ACO) is integrated with the self-organizing map (SOM) for image segmentation. A comparative study with the combination of ACO and Simple Competitive Learning (SCL) is provided. ACO follows a learning mechanism through pheromone updates. In addition, pheromone and heuristic information are normalized and the effects on the results are investigated in this report. Preliminary experimental results indicate that the normalization of the parameters can improve the image segmentation results.