智能系统学报2024，Vol.19Issue(2)：381-391,11.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
An improved remora optimization algorithm for multilevel thresholding image segmentation using an entropy measure
摘要
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 op-timization 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 al-gorithm'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 com-parison with other well-known algorithms,WLROA yielded better segmentation results and proved effective in accur-ately processing complex images.关键词
图像处理/多阈值分割/䲟鱼优化算法/最小交叉熵/透镜成像反向学习/自适应权重因子/全局优化/遥感图像Key words
image processing/multilevel thresholding segmentation/remora optimization algorithm/minimum cross en-tropy/lens opposite learning/adaptive weight factor/global optimization/remote sensing image分类
信息技术与安全科学引用本文复制引用
刘庆鑫,李霓,贾鹤鸣,齐琦..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):381-391,11.基金项目
国家自然科学基金项目(11861030) （11861030）
海南省自然科学基金项目(621RC511,2019RC176) （621RC511,2019RC176）
海南省研究生创新科研课题(Qhys2021-190). （Qhys2021-190）
