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