Abstract
Ant Colony Optimization (ACO) is a newly proposed intelligent algorithm for solving discrete optimization problems such as the Travelling Salesman Problem (TSP). In this paper we introduce a novel ACO-based clustering algorithm and exploit its application in image segmentation. Unlike traditional ACO which is mainly based on probabilistic and hard path choosing, the proposed method utilizes a soft and fuzzy scheme. In detail, every pixel in the image is viewed as an ant and the calculation of membership function is based on heuristic and pheromone information on each cluster center. In addition, memberships are modified to include spatial information which can further improve the algorithm performance for image segmentation. Experiments are taken to examine the performance of ACO-based fuzzy clustering algorithm and segmentation results indicate that the proposed approach has the potential of becoming an established clustering method for image segmentation.
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© 2009 Springer-Verlag Berlin Heidelberg
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Yu, Z., Yu, W., Zou, R., Yu, S. (2009). On ACO-Based Fuzzy Clustering for Image Segmentation. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01510-6_81
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DOI: https://doi.org/10.1007/978-3-642-01510-6_81
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01509-0
Online ISBN: 978-3-642-01510-6
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