计算机应用与软件2025，Vol.42Issue(5)：231-237,7.DOI:10.3969/j.issn.1000-386x.2025.05.032
改进麻雀算法优化多阈值图像分割
IMPROVED SPARROW SEARCH ALGORITHM TO OPTIMIZE MULTI-THRESHOLD IMAGE SEGMENTATION
摘要
Abstract
Because the traditional Otsu multi-threshold image segmentation algorithm usually takes too much time to find the optimal segmentation threshold.Therefore,this paper proposes an improved sparrow search algorithm(SSA)to shorten the time cost.Based on the traditional SSA,chaos initialization strategy,adaptive weighting,reverse learning strategy,and Levy flight mechanism were introduced to perform multi-threshold image segmentation.It was compared with the image segmentation results of algorithms such as PSO,GWO,SSA and ISSA.Experimental results show that the algorithm greatly shortens the running time of the traditional multi-threshold Otsu image segmentation algorithm,and improves the accuracy of image segmentation,which has certain practical value.关键词
多阈值Otsu/麻雀搜索算法/混沌初始化/自适应权重/反向学习/Levy飞行Key words
Multi-threshold Otsu/Sparrow search algorithm/Chaos initialization/Adaptive weight/Reverse learning/Levy flight分类
信息技术与安全科学引用本文复制引用
马远阳,黄福珍..改进麻雀算法优化多阈值图像分割[J].计算机应用与软件,2025,42(5):231-237,7.基金项目
上海市电站自动化技术重点实验室项目(13DZ2273800). （13DZ2273800）
