[1]刘庆鑫,李霓,贾鹤鸣,等.改进䲟鱼优化算法和熵测度的图像多阈值分割[J].智能系统学报,2024,19(2):381-391.[doi:10.11992/tis.202205018]
LIU Qingxin,LI Ni,JIA Heming,et al.An improved remora optimization algorithm for multilevel thresholding image segmentation using an entropy measure[J].CAAI Transactions on Intelligent Systems,2024,19(2):381-391.[doi:10.11992/tis.202205018]
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LIU Qingxin,LI Ni,JIA Heming,et al.An improved remora optimization algorithm for multilevel thresholding image segmentation using an entropy measure[J].CAAI Transactions on Intelligent Systems,2024,19(2):381-391.[doi:10.11992/tis.202205018]
改进䲟鱼优化算法和熵测度的图像多阈值分割
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
19
期数:
2024年第2期
页码:
381-391
栏目:
学术论文—智能系统
出版日期:
2024-03-05
- Title:
- An improved remora optimization algorithm for multilevel thresholding image segmentation using an entropy measure
- Keywords:
- image processing; multilevel thresholding segmentation; remora optimization algorithm; minimum cross entropy; lens opposite learning; adaptive weight factor; global optimization; remote sensing image
- 分类号:
- TP391.41;TP18
- 文献标志码:
- 2023-11-30
- 摘要:
- 针对传统图像多阈值分割方法存在效率低、分割质量差等问题，提出一种改进?鱼优化算法并结合熵测度(weight lens remora optimization algorithm, WLROA)的图像多阈值分割方法。针对?鱼优化算法易陷入局部极值等缺陷，引入透镜成像反向学习策略，生成透镜反向解来增加种群多样性，进而提高算法跳出局部极值能力；提出一种自适应权重因子，对个体位置进行自适应扰动，提高算法探索能力。以最小化交叉熵作为优化目标，利用WLROA确定最小交叉熵并获得相应分割阈值。选取部分伯克利大学分割数据集图像和遥感图像测试提出算法的分割性能，测试结果表明，WLROA与其他知名算法相比具有更好的分割效果，能够有效实现复杂图像的精确处理。
- Abstract:
- To improve the poor segmentation quality of traditional image thresholding segmentation techniques, this study proposes an image multilevel thresholding segmentation method. This method is based on an improved remora optimization algorithm and entropy measure, specifically called the weight lens remora optimization algorithm (WLROA). First, lens opposition-based learning was used to generate the lens opposite solution. This approach bolstered population diversity and improved the algorithm’s ability to overcome local optimal solutions. Furthermore, an adaptive weight factor was introduced to perturb the individuals’ positions appropriately. This modification aimed to improve the algorithm’s exploratory ability. The optimization objective was to minimize cross entropy. To achieve this, WLROA was used to determine the minimum cross entropy and obtain the corresponding thresholds. A selection of images from the Berkeley segmentation data set and remote sensing images were selected to assess the segmentation performance of the proposed algorithm. These results were then compared with those from other methods. The results revealed that, in comparison with other well-known algorithms, WLROA yielded better segmentation results and proved effective in accurately processing complex images.
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备注/Memo
收稿日期:2022-05-17。
基金项目:国家自然科学基金项目（11861030）；海南省自然科学基金项目（621RC511，2019RC176）；海南省研究生创新科研课题（Qhys2021-190）.
作者简介:刘庆鑫，硕士研究生，主要研究方向为启发式优化算法、图像处理。E-mail：qxliu@hainanu.edu.cn;李霓，副教授，主要研究方向为生存分析、纵向数据分析、复杂删失数据的统计推断。主持国家自然科学基金项目2项，发表学术论文20余篇。E-mail：lini@hainnu.edu.cn;齐琦，副研究员，博士生导师，博士，中国计算机学会人工智能与模型识别、计算经济学专委会委员。主要研究方向为组合优化、算法博弈、计算智能以及数据挖掘和机器学习。参与国家重点研发计划和海南省重大科技计划等3项，发表学术论文20多篇。 E-mail：qqi@hainanu.edu.cn
通讯作者:齐琦. E-mail：qqi@hainanu.edu.cn
更新日期/Last Update:
1900-01-01