Abstract
Clustering is the process of partitioning a given set of pixels into a number of homogenous clusters based on a similarity criterion. The clustering problem is a difficult optimization problem for two main reasons: first the search space of the optimization is too large, second the clustering objective function is typically non convex and thus may exhibit a large number of local minima. In this paper we propose the use of the Ant Colony System (ACS) [1] to solve the clustering problem.
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M. Dorigo, and L. Gambardella. Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem, IEEE Transactions on Evolutionary Computation 1(1): 53–66, 1997.
S. Geman. and D. Geman. Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images, IEEE Transactions on Pattern Analysis and Machine Intelligence 6(6): 721–741, 1984.
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© 2002 Springer-Verlag Berlin Heidelberg
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Ouadfel, S., Batouche, M., Garbay, C. (2002). Ant Colony System for Image Segmentation Using Markov Random Field. In: Dorigo, M., Di Caro, G., Sampels, M. (eds) Ant Algorithms. ANTS 2002. Lecture Notes in Computer Science, vol 2463. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45724-0_32
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DOI: https://doi.org/10.1007/3-540-45724-0_32
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