Edge and Characteristic Subset Selection in Images Using ACO

The Ant Colony Optimization (ACO) is a metaheuristic, inspired by the behavior of real ants in their search for the shortest paths to food sources. It has recently attracted a lot of attention and has been successfully applied to a number of different optimization problems. Due to the importance of the feature selection problem and the potential of ACO, this paper presents a novel method that utilizes the ACO algorithm to implement a feature subset search procedure. Initial results obtained using the classification of speech segments is very promising. ACO approach in solving complicated optimization problems is relatively new. The main advantage of swarm intelligence approach is that system of simple communicating agents is capable of solving complex problems. Ant Colony Optimization (ACO) being a branch of swarm intelligence is here considered and its use for important image processing application is investigated.