Multi-level threshold selection based on artificial bee colony algorithm and maximum entropy for image segmentation

Authors:
Yonghao Xiao;Yunfei Cao;Weiyu Yu;Jing Tian
Affiliations:
School of Electronic and Information Engineering, South China University of Technology, Guangzhou, 510641, China/ Foshan University, Foshan, 528000, China.;School of Electronic and Information Engineering, South China University of Technology, Guangzhou, 510641, China.;School of Electronic and Information Engineering, South China University of Technology, Guangzhou, 510641, China/ Provincial Key Laboratory for Computer Information Processing Technology, Soochow ...;BLK 523, Jelapang Road, 670523, Singapore
Venue:
International Journal of Computer Applications in Technology
Year:
2012

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Abstract

Image threshold segmentation based on artificial bee colony algorithm (ABCA) and maximum entropy is presented in this paper. The entropy function is simplified with several parameters. The ABC is applied to search the maximum value of entropy function. According to the maximum function value, the optimal image thresholds are obtained. Experimental results are provided to demonstrate the superior performance of the proposed approach.