Abstract
In this paper, we present a novel method for unsupervised image segmentation. Image segmentation is cast as a clustering problem, which aims to partition 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. Ant Colony Optimization is a recent multi-agent approach based on artificial ants for solving hard combinatorial optimization problems. We propose the use of the Max-Min Ant System (MMAS) to solve the clustering problem in the field of image segmentation. Each pixel within the image is mapped to its closest cluster taking into account its immediate neighborhood. The obtained results are encouraging and prove the feasibility of the proposed algorithm.
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Ouadfel, S., Batouche, M. (2002). Unsupervised Image Segmentation Using a Colony of Cooperating Ants. In: Bülthoff, H.H., Wallraven, C., Lee, SW., Poggio, T.A. (eds) Biologically Motivated Computer Vision. BMCV 2002. Lecture Notes in Computer Science, vol 2525. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36181-2_11
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DOI: https://doi.org/10.1007/3-540-36181-2_11
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