International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 7, No. 5, 8 Apr. 2015
Cover page and Table of Contents: PDF (size: 353KB)
An Image Thresholding Approach Based on Ant Colony Optimization Algorithm Combined with Genetic Algorithm
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Author(s)
Zhiwei Ye
1,*
MingWei Wang
1
Huazhong Jin
1
Wei Liu
1
XuDong Lai
2
Index Terms
Image Segmentation, Image Thresholding, Optimization, 2-D Fisher Criteria, Ant Colony Optimization Algorithm, Genetic Algorithm
Abstract
Image segmentation is a basic work in the field of image analysis and computer vision. Thresholding is one of the simplest methods of image segmentation. In general, thresholding approaches based on 1-D histogram do not make use of any space adjacent information of the image, thus it is often ruined by noise; thus, thresholding methods based on 2-D histogram are put forward. These methods have better segmentation performance, but heavy computation is required with these methods. In the paper, to improve the running efficiency of thresholding methods based 2D histogram, ant colony optimization algorithm combined with genetic algorithm are employed to speed up these methods, which view 2-D histogram based thresholding as a kind of optimization problem. The proposed method has been conducted on some images. Experiments results display that the proposed approach is able to achieve improved search performance which is an efficient method and suitable for real time applications.
Cite This Paper
Zhiwei Ye, MingWei Wang, Huazhong Jin, Wei Liu, XuDong Lai, "An Image Thresholding Approach Based on Ant Colony Optimization Algorithm Combined with Genetic Algorithm", International Journal of Intelligent Systems and Applications(IJISA), vol.7, no.5, pp.8-15, 2015. DOI:10.5815/ijisa.2015.05.02
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