Title: 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

Addresses: 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 University, Suzhou, Jiangsu, 215006, China. ' BLK 523, Jelapang Road, 670523, Singapore

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.

Keywords: image threshold; maximum entropy; artificial bee colony; ABC; image segmentation; threshold selection.

DOI: 10.1504/IJCAT.2012.047159

International Journal of Computer Applications in Technology, 2012 Vol.43 No.4, pp.343 - 350

Received: 08 May 2021
Accepted: 12 May 2021

Published online: 31 May 2012 *

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