Image retrieval is a system for browsing, searching the query image and retrieving similar images from large databases. A wide variety of features can be used for image retrieval. This process selects a subset of relevant features from a group of features of the image. It also helps to acquire better understanding about the image by describing which the important features are. The accuracy can be improved by increasing the number of features selected. But this increases the complexity of the retrieval system. The performance can be improved by removing the irrelevant and redundant features from taking into consideration. This is known as optimization. Many optimization techniques can be used. Ant Colony Optimization (ACO) is the technique proposed in this paper. With ACO, the image features are selected and images are retrieved from databases with high accuracy.

1. Haarlick.R.M,1979. “Statistical and structural approaches to texture”, IEEE Transactions on system, man and cybematics 67, 786-804
2. D J Hemanth, D Selvathi & J Anitha ,2010 “Artificial Intelligence Techniques for Medical Image Analysis: Basics, Methods, Applications”, VDM –Verlag, ISBN-9783639248258
3. Chuen-Horng Lin , Rong-Tai Chen , Yung-Kuan Chan, 2009. “A smart content-based image retrieval system based on color and texture feature”, Image and Vision Computing 27pp658–665
4. J.Anitha, C. Kezi Selva Vijila & D.Jude Hemanth, 2010. “A hybrid genetic algorithm based fuzzy approach for abnormal retinal image classification”, International Journal of Cognitive Informatics and Natural Intelligence”, Vol.4, No.3, pp 29-43,
5. Yas Abbas Alsultanny, Musbah M. Aqel,2003. “Pattern Recognition using Multilayer Neural-Genetic Algorithm”, Neurocomputing 51, pp237-247
6. D.Jude Hemanth, C. Kezi Selva Vijila & J.Anitha, 2010. “Performance improved PSO based modified counter propagation neural network for abnormal MR brain image classification”, Int. J. Advance. Soft Comput. Appl., Vol.2, No.1, March.
7. Krishna Chandramouli, Ebroul Izquierdo, 2006. “Image Classification using Chaotic Particle Swarm Optimization ”, IEEE Transactions on Image Processing pp3001-3004
8. Auralia I. Edwards, Andries P. Engelbrecht, 2005. “Comparing Different Optimization Strategies for Nonlinear Mapping and Mapping Large Datasets using Neural Networks”, World Congress on Evolutionary Computation, pp306-313
9. Xiangyang Wang, Jie Yang , Xiaolong Teng, Weijun Xia, Richard Jensen,2007. “Feature Selection based on Rough sets and Particle Swarm Optimization”, Pattern Recognition Letters 28, pp 459-471
10. Anna Veronica Baterina, Carlos Oppus, 2010. “Image Edge Detection Using Ant Colony Optimization”, International Journal of Circuits, Systems and Signal Processing, Issue 2, Vol.4,
11. J.Jaya, K. Thanushkodi,2010. “Segmentation of MR Brain Tumor Using Parallel ACO”, International Journal of Computer and Network Security, Vol.2, No.6, pp150-153
12. Fizazi Hadria, Hannane Amir Mokhtar, “Remote Sensing Image Classification using Ant Colony Optimization”

Optimization Genetic Algorithm (GA) Particle Swarm Optimization (PSO) Ant Colony Optimization (ACO)