DocumentCode :
2711021
Title :
Edge and Characteristic Subset Selection in Images Using ACO
Author :
Venkatesan, S. ; Karnan, M.
Author_Institution :
Dept. of Comput. Sci. & Eng., Anna Univ. Coimbatore, Coimbatore, India
fYear :
2010
fDate :
7-10 May 2010
Firstpage :
369
Lastpage :
372
Abstract :
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.
Keywords :
edge detection; feature extraction; particle swarm optimisation; ACO; ant colony optimization; characteristic subset selection; edge selection; feature selection; image processing; swarm intelligence; Research and development; Ant Colony Optimization; Edge calculation; Swarm Intelligence; ant systems; pattern recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Research and Development, 2010 Second International Conference on
Conference_Location :
Kuala Lumpur
Print_ISBN :
978-0-7695-4043-6
Type :
conf
DOI :
10.1109/ICCRD.2010.95
Filename :
5489577
Link To Document :
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