Simultaneous Parameter Estimation and Segmentation of Gibbs Random Fields Using Simulated Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Ant algorithms for discrete optimization
Artificial Life
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Artificial Ants to Extract Leaf Outlines and Primary Venation Patterns
ANTS '08 Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence
Review: A review of ant algorithms
Expert Systems with Applications: An International Journal
Heuristic Search for Cluster Centroids: An Ant-Based Approach for FCM Initialization
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Ant algorithms for image feature extraction
Expert Systems with Applications: An International Journal
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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.