应用改进布谷鸟算法优化多阈值图像分割  

Image segmentation of multilevel threshold based on improved cuckoo search algorithm

作　　者：吴禄慎[1] 程伟 胡赟 WU Lu-shen;CHENG Wei;HU Yun(School of Mechanical and Electrical Engineering,Nanchang University,Nanchang 330031,China)

机构地区：[1]南昌大学机电工程学院,南昌330031

出　　处：《吉林大学学报（工学版）》2021年第1期358-369,共12页Journal of Jilin University:Engineering and Technology Edition

基　　金：国家自然科学基金项目(51065021,51365037,51705229)。

摘　　要：针对传统多阈值图像分割方法在寻找最优阈值过程中存在计算量大、计算时间长的问题,提出了一种基于改进布谷鸟算法的多阈值图像分割方法。首先,将教与学搜索策略引入布谷鸟算法,提高了算法的局部搜索能力;其次,选择当前种群中适应度值较优的精英解构建精英库并随机选择精英解指导搜索方向,强化优势经验的学习;最后,引入模拟退火机制选择鸟巢位置,有效避免了个体在寻优过程中陷入局部最优。选择了多幅不同类型的复杂多目标图像进行分割实验,并与布谷鸟算法、蛙跳算法、教与学优化算法及广义反向粒子群与引力搜索混合算法的分割结果进行对比分析。实验结果表明,该方法在分割准确性、计算时间和收敛性上均优于对比算法,能快速有效地解决复杂多目标图像的多阈值分割问题。In order to overcome the issue of large amount of calculation and long computing time of the traditional multi-threshold image segmentation methods,a multi-threshold image segmentation method based on improved cuckoo search algorithm is proposed. Firstly,the teaching-learning search strategy is introduced into the cuckoo algorithm to improve local search ability of the algorithm. Secondly,the elite solution with better fitness value in the current population is selected to construct the elite database,and the elite solution is randomly selected to guide the search direction,so as to strengthen the advantage of experience learning. Finally,the simulated annealing mechanism is introduced to select the bird′s nest location,which can effectively avoid the individuals falling into the local optimum in the process of optimization. Several different types of complex multi-target images are selected for segmentation experiments in comparison with those of cuckoo search algorithm(CS),shuffled frog leaping algorithm(SFL),teaching-learning-based optimization(TLBO)and hybrid PSOGSA with generalized oppositingbased learning(GOPSOGSA). Experimental results show that the proposed method is superior to the contrasted algorithms in segmentation accuracy, running time and convergence. It can quickly and effectively solve the multi-threshold segmentation problem of complex multi-target images.

关 键 词：图像分割 多阈值分割 布谷鸟算法 教与学搜索策略 精英解 模拟退火机制 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]