DocumentCode :
467767
Title :
A Study on Image Segmentation by an Improved Adaptive Algorithm
Author :
Li, Qing ; He, Wen-Hao ; Jiang, Han-Hong ; Li, Xuan-Zhong
Author_Institution :
Wuhan Univ. of Technol., Wuhan
Volume :
3
fYear :
2007
fDate :
19-22 Aug. 2007
Firstpage :
1570
Lastpage :
1573
Abstract :
In the first place, an improvement was made on crossover and mutation of adaptive genetic algorithm (AGA) to let the crossover probability and mutation probability adapt nonlinearly. Then a comparison was made between Improved adaptive genetic algorithm (IAGA) and adaptive genetic algorithm (AGA) in segmentation time and adaptive function curve. The results indicated that IAGA can give attention to the main information of experiment images. And much less time was used by the algorithm. The process of searching for global optimum also became more stable than AGA.
Keywords :
genetic algorithms; image segmentation; probability; adaptive function curve; crossover probability; image segmentation; improved adaptive genetic algorithm; mutation probability; Adaptive algorithm; Convergence; Cybernetics; Genetic algorithms; Genetic engineering; Genetic mutations; Helium; Image segmentation; Machine learning; Robustness; Crossover; Image segmentation; Improved adaptive genetic algorithm (IAGA); Mutation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics, 2007 International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-0973-0
Electronic_ISBN :
978-1-4244-0973-0
Type :
conf
DOI :
10.1109/ICMLC.2007.4370395
Filename :
4370395
Link To Document :
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