The social insect metaphor for solving problems has become an emerging topic in the recent years. This approach emphasizes on direct or indirect interactions among simple agents. Swarm Intelligence is the collective behavior of decentralized [8], self-organized [4] system whereby the collective behavior of agent interacting locally with the environment causes coherent global pattern to emerge. Classification is a computational procedure that sorts the image into groups according to their similarities [5]. Images can be similar but to measure the similarity pixel-to-pixel comparison is made. Numerous methods for classification have been developed. Exploring new methods to increase classification accuracies have been a key topic. This paper explores the swarm computing methods called Ant Colony Optimization (ACO) to classify imagery.

1. Ling Chen, Bolun Chen and Yixin Chen, 2011, "Image Feature Selection Based on Ant Colony Optimization", AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence, pp. 580-589.
2. Simranjeet Kaur, Prateek Agarwal, Rajbir Singh Rana, 2011" Ant Colony Optimization: A Technique used for Image Processing, Dept. of CSE, Lovely Professional University, IJCST Vol. 2, Issue 2.
3. Lintao Wen, Qian Yin, Ping Guo, 2009, "Ant Colony Optimization algorithm for feature selection and Classification of multispectral remote sensing image", 2nd International congress on Image and Signal Processing, CISP'09.
4. Rebika Rai, Tejbanta Singh Chinghtam, M. K. Ghose, 2009, "Optimization of Autonomous Multi-Robot Path Planning & Navigation using Swarm Intelligence", In National Conference on LEAN Manufacturing Implementations : The future of Process Industries (LEMAN '2009)".
5. Xiaoping Liu, Xia Li, Lin Liu, Jinqiang He and Bin Ai, 2008, "An Innovative method to classify Remote-Sensing Images using Ant colony Optimization", IEEE transactions on geoscience and remote sensing, vol. 46, no. 12.
6. T. Piatrik and E. Izquierdo, 2008, "An Application of Ant Colony Optimization to Image Clustering," in Proc. K-Space Jamboree Workshop.
7. S. N. Omkar, Manoj Kumar M, Dheevatsa Mudigere, Dipti Muley, 2007, "Urban Satellite Image Classification using Biologically Inspired Techniques", In: IEEE International Symposium on Industrial Electronics.
8. H. Liu, F. Hussain, C. L. Tan, and M. Dash, 2002, "Discretization: An enabling technique," Data Mining Knowl. Discovery, vol. 6, no. 4, pp. 393–423.
9. R. Eberhart, Y. Shi, and J. Kennedy, 2001, Swarm Intelligence. San Francisco, CA: Morgan Kaufmann.
10. M. Dorigo and L. M. Gambardella, 1997, "Ant colony system: A cooperative learning approach to the traveling salesman problem," IEEE Trans. Evol. Comput. vol. 1, no. 1, pp. 53–66.
11. H. Gutowitz, "Complexity-seeking ants", 1993, In Proc. of the Third European Conference on Artificial Life.
12. M. Dorigo, 1992, "Optimization, learning and natural algorithms," Ph. D. dissertation, Dept. Electron. , Politecnico di Milano, Milan, Italy.

Ant Colony Optimization (aco) Artificial Intelligence (ai) Swarm Intelligence (si)