Multi-threshold image segmentation based on improved sparrow search algorithm

被引：34

作者：

Lyu X. ^{[1
,2
]}

Mu X. ^{[1
]}

Zhang J. ^{[2
]}

机构：

[1] Operational Support Academy, Rocket Force University of Engineering, Xi'an

[2] Beijing Institute of Remote Sensing Equipment, Beijing

来源：

Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2021年 / 43卷 / 02期

关键词：

Image segmentation; Improved sparrow search algorithm (ISSA); Kapur entropy; Multi-threshold; Otsu;

D O I：

10.12305/j.issn.1001-506X.2021.02.05

中图分类号：

学科分类号：

摘要：

To solve the problems of low segmentation accuracy, large calculation amount and slow segmentation speed in the traditional multi-threshold image segmentation methods, a multi-threshold image segmentation method based on improved sparrow search algorithm (ISSA) is proposed. First, the sparrow search algorithm (SSA) is optimized based on the flight behavior in the bird swarm algorithm (BSA), and four types of benchmark functions are used to evaluate the optimization performance of ISSA. Then, the between-class variance and Kapur entropy are used to perform the multi-threshold image segmentation, and the segmentation results of the two methods are compared. Finally, using PSNR, objective function value and standard deviation as evaluation criteria, ISSA is compared with the existing segmentation algorithms. The results show that ISSA has better search ability and development ability, and it has a significant improvement in terms of segmentation speed and accuracy. © 2021, Editorial Office of Systems Engineering and Electronics. All right reserved.

引用

页码：318 / 327

页数：9

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