Cargando…
Symmetric cross-entropy multi-threshold color image segmentation based on improved pelican optimization algorithm
To address the problems of low accuracy and slow convergence of traditional multilevel image segmentation methods, a symmetric cross-entropy multilevel thresholding image segmentation method (MSIPOA) with multi-strategy improved pelican optimization algorithm is proposed for global optimization and...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10309640/ https://www.ncbi.nlm.nih.gov/pubmed/37384625 http://dx.doi.org/10.1371/journal.pone.0287573 |
_version_ | 1785066481802805248 |
---|---|
author | Zhang, Chuang Pei, Yue-Han Wang, Xiao-Xue Hou, Hong-Yu Fu, Li-Hua |
author_facet | Zhang, Chuang Pei, Yue-Han Wang, Xiao-Xue Hou, Hong-Yu Fu, Li-Hua |
author_sort | Zhang, Chuang |
collection | PubMed |
description | To address the problems of low accuracy and slow convergence of traditional multilevel image segmentation methods, a symmetric cross-entropy multilevel thresholding image segmentation method (MSIPOA) with multi-strategy improved pelican optimization algorithm is proposed for global optimization and image segmentation tasks. First, Sine chaotic mapping is used to improve the quality and distribution uniformity of the initial population. A spiral search mechanism incorporating a sine cosine optimization algorithm improves the algorithm’s search diversity, local pioneering ability, and convergence accuracy. A levy flight strategy further improves the algorithm’s ability to jump out of local minima. In this paper, 12 benchmark test functions and 8 other newer swarm intelligence algorithms are compared in terms of convergence speed and convergence accuracy to evaluate the performance of the MSIPOA algorithm. By non-parametric statistical analysis, MSIPOA shows a greater superiority over other optimization algorithms. The MSIPOA algorithm is then experimented with symmetric cross-entropy multilevel threshold image segmentation, and eight images from BSDS300 are selected as the test set to evaluate MSIPOA. According to different performance metrics and Fridman test, MSIPOA algorithm outperforms similar algorithms in global optimization and image segmentation, and the symmetric cross entropy of MSIPOA algorithm for multilevel thresholding image segmentation method can be effectively applied to multilevel thresholding image segmentation tasks. |
format | Online Article Text |
id | pubmed-10309640 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-103096402023-06-30 Symmetric cross-entropy multi-threshold color image segmentation based on improved pelican optimization algorithm Zhang, Chuang Pei, Yue-Han Wang, Xiao-Xue Hou, Hong-Yu Fu, Li-Hua PLoS One Research Article To address the problems of low accuracy and slow convergence of traditional multilevel image segmentation methods, a symmetric cross-entropy multilevel thresholding image segmentation method (MSIPOA) with multi-strategy improved pelican optimization algorithm is proposed for global optimization and image segmentation tasks. First, Sine chaotic mapping is used to improve the quality and distribution uniformity of the initial population. A spiral search mechanism incorporating a sine cosine optimization algorithm improves the algorithm’s search diversity, local pioneering ability, and convergence accuracy. A levy flight strategy further improves the algorithm’s ability to jump out of local minima. In this paper, 12 benchmark test functions and 8 other newer swarm intelligence algorithms are compared in terms of convergence speed and convergence accuracy to evaluate the performance of the MSIPOA algorithm. By non-parametric statistical analysis, MSIPOA shows a greater superiority over other optimization algorithms. The MSIPOA algorithm is then experimented with symmetric cross-entropy multilevel threshold image segmentation, and eight images from BSDS300 are selected as the test set to evaluate MSIPOA. According to different performance metrics and Fridman test, MSIPOA algorithm outperforms similar algorithms in global optimization and image segmentation, and the symmetric cross entropy of MSIPOA algorithm for multilevel thresholding image segmentation method can be effectively applied to multilevel thresholding image segmentation tasks. Public Library of Science 2023-06-29 /pmc/articles/PMC10309640/ /pubmed/37384625 http://dx.doi.org/10.1371/journal.pone.0287573 Text en © 2023 Zhang et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Zhang, Chuang Pei, Yue-Han Wang, Xiao-Xue Hou, Hong-Yu Fu, Li-Hua Symmetric cross-entropy multi-threshold color image segmentation based on improved pelican optimization algorithm |
title | Symmetric cross-entropy multi-threshold color image segmentation based on improved pelican optimization algorithm |
title_full | Symmetric cross-entropy multi-threshold color image segmentation based on improved pelican optimization algorithm |
title_fullStr | Symmetric cross-entropy multi-threshold color image segmentation based on improved pelican optimization algorithm |
title_full_unstemmed | Symmetric cross-entropy multi-threshold color image segmentation based on improved pelican optimization algorithm |
title_short | Symmetric cross-entropy multi-threshold color image segmentation based on improved pelican optimization algorithm |
title_sort | symmetric cross-entropy multi-threshold color image segmentation based on improved pelican optimization algorithm |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10309640/ https://www.ncbi.nlm.nih.gov/pubmed/37384625 http://dx.doi.org/10.1371/journal.pone.0287573 |
work_keys_str_mv | AT zhangchuang symmetriccrossentropymultithresholdcolorimagesegmentationbasedonimprovedpelicanoptimizationalgorithm AT peiyuehan symmetriccrossentropymultithresholdcolorimagesegmentationbasedonimprovedpelicanoptimizationalgorithm AT wangxiaoxue symmetriccrossentropymultithresholdcolorimagesegmentationbasedonimprovedpelicanoptimizationalgorithm AT houhongyu symmetriccrossentropymultithresholdcolorimagesegmentationbasedonimprovedpelicanoptimizationalgorithm AT fulihua symmetriccrossentropymultithresholdcolorimagesegmentationbasedonimprovedpelicanoptimizationalgorithm |