Application of Improved Satin Bowerbird Optimizer in Image Segmentation

Aiming at the problems of low optimization accuracy and slow convergence speed of Satin Bowerbird Optimizer (SBO), an improved Satin Bowerbird Optimizer (ISBO) based on chaotic initialization and Cauchy mutation strategy is proposed. In order to improve the value of the proposed algorithm in enginee...

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Published inFrontiers in plant science Vol. 13; p. 915811
Main Authors Li, Linguo, Qian, Shunqiang, Li, Zhangfei, Li, Shujing
Format Journal Article
LanguageEnglish
Published Switzerland Frontiers Media S.A 06.05.2022
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ISSN1664-462X
1664-462X
DOI10.3389/fpls.2022.915811

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Summary:Aiming at the problems of low optimization accuracy and slow convergence speed of Satin Bowerbird Optimizer (SBO), an improved Satin Bowerbird Optimizer (ISBO) based on chaotic initialization and Cauchy mutation strategy is proposed. In order to improve the value of the proposed algorithm in engineering and practical applications, we apply it to the segmentation of medical and plant images. To improve the optimization accuracy, convergence speed and pertinence of the initial population, the population is initialized by introducing the Logistic chaotic map. To avoid the algorithm falling into local optimum (prematurity), the search performance of the algorithm is improved through Cauchy mutation strategy. Based on extensive visual and quantitative data analysis, this paper conducts a comparative analysis of the ISBO with the SBO, the fuzzy Gray Wolf Optimizer (FGWO), and the Fuzzy Coyote Optimization Algorithm (FCOA). The results show that the ISBO achieves better segmentation effects in both medical and plant disease images.
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Edited by: Yu Xue, Nanjing University of Information Science and Technology, China
This article was submitted to Sustainable and Intelligent Phytoprotection, a section of the journal Frontiers in Plant Science
Reviewed by: Daqing Wu, Shanghai Ocean University, China; Xijian Fan, Nanjing Forestry University, China; Jianbo Meng, Chongqing University, China
ISSN:1664-462X
1664-462X
DOI:10.3389/fpls.2022.915811