DocumentCode
2340260
Title
Ant colony optimization for image segmentation
Author
Wang, Xiao-Nian ; Feng, Yuan-Jing ; Feng, Zu-Ren
Author_Institution
Syst. Eng. Inst., Xi´´an Jiao Tong Univ., China
Volume
9
fYear
2005
fDate
18-21 Aug. 2005
Firstpage
5355
Abstract
It is found that the multistage decision algorithm for image segmentation with active contour model (ACM) is similar to ant colony optimization (ACO). By means of constructing solution space and heuristic information, a new algorithm based on ACM is proposed in the paper, which uses ACO to search for the best path in a constrained region. This algorithm that provides a new approach to obtain precise contour, is proved to be convergent with probability one, and will reach the best feasible boundary with minimum energy function value. Moreover, this algorithm can also be used to solve other revised ACM problems. The simulation results show that the proposed approach is more effective than the genetic algorithm in literature (Mishraa et al., 2003).
Keywords
genetic algorithms; image segmentation; active contour model; ant colony optimization; constrained region; genetic algorithm; heuristic information; image segmentation; multistage decision algorithm; Active contours; Ant colony optimization; Computational modeling; Equations; Genetic algorithms; Greedy algorithms; Image converters; Image segmentation; Optimization methods; Systems engineering and theory; Active Contour Model; Ant Colony Optimization; Image segment;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on
Conference_Location
Guangzhou, China
Print_ISBN
0-7803-9091-1
Type
conf
DOI
10.1109/ICMLC.2005.1527890
Filename
1527890
Link To Document