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.
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Saatchi, S., Hung, CC. (2007). Using Ant Colony Optimization and Self-organizing Map for Image Segmentation. In: Gelbukh, A., Kuri Morales, Á.F. (eds) MICAI 2007: Advances in Artificial Intelligence. MICAI 2007. Lecture Notes in Computer Science(), vol 4827. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76631-5_54
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DOI: https://doi.org/10.1007/978-3-540-76631-5_54
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