基于鸽群优化算法的图像分割方法研究
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摘要
图像分割是一类需要在非线性参数空间中寻求最优解的有约束非线性优化问题.为提高此类优化问题的寻优精度,提出了一种基于鸽群优化算法的图像分割方法.首先以分割阈值为优化变量,将图像分割建模为以最大间类方差为优化目标,以像素概率分布有限为约束条件的非线性优化问题;随后,以随机的分割阈值作为迭代初值,采用鸽群优化算法(PIO)求解最优参数;最后,利用所得最优解作为最佳阈值实现图像分割.为验证方法的有效性,分别对具有两类不同特征的图片进行分割实验,并采用重叠度及时间效率对算法进行评估,进一步与PSO、KSW智能优化算法对比.结果表明,该算法重叠度最高,运算时间最短.并且对算法中的参数进行修改,将图像分割结果进一步优化.
Image segmentation was a kind of constrained nonlinear optimization problem that aimed to seek the optimal solution in nonlinear parameter space. In order to improve the precision of the optimization problem,an image segmentation method based on pigeon group optimization algorithm was proposed. Firstly the segmentation threshold was used as the optimization variable,and the image segmentation was modeled as a nonlinear optimization problem with the optimal threshold equation as the objective function,and the inter-class variance and the w_0 and w_1 ranged as the constraints. Then,using random segmentation threshold as the initial value of iteration,the optimal parameters were solved by the pigeon group optimization algorithm( PIO). In order to verify the validity of this method,two kinds of images with different features were divided into experiments,and the algorithm was evaluated by overlapping degree and time efficiency. The results showed that the algorithm had the highest degree of overlap and the shortest operation time.
Image segmentation was a kind of constrained nonlinear optimization problem that aimed to seek the optimal solution in nonlinear parameter space. In order to improve the precision of the optimization problem,an image segmentation method based on pigeon group optimization algorithm was proposed. Firstly the segmentation threshold was used as the optimization variable,and the image segmentation was modeled as a nonlinear optimization problem with the optimal threshold equation as the objective function,and the inter-class variance and the w_0 and w_1 ranged as the constraints. Then,using random segmentation threshold as the initial value of iteration,the optimal parameters were solved by the pigeon group optimization algorithm( PIO). In order to verify the validity of this method,two kinds of images with different features were divided into experiments,and the algorithm was evaluated by overlapping degree and time efficiency. The results showed that the algorithm had the highest degree of overlap and the shortest operation time.
引文
[1]张健.机器视觉中的尺度可控图像分割方法[J].通信技术,2018,51(3):583-587.
[2]杨进,郑允,马良.改进的猫群算法求解TSP[J].计算机应用研究,2017,34(12):3607-3610.
[3]LONG W,LIANG X M,CAI S H,et al.A modified augmented Lagrangian with improved grey wolf optimization to constrained optimization problems[J].Neural computing and applications,2017,28(S1):421-438.
[4]张军国,冯文钊,胡春鹤,等.无人机航拍林业虫害图像分割复合梯度分水岭算法[J].农业工程学报,2017,33(14):93-99.
[5]LIANG Y F,SUN L,SER W,et al.Hybrid threshold optimization between global image and local regions in image segmentation for melasma severity assessment[J].Multidimensional systems and signal processing,2017,28(3):977-994.
[6]SNCHEZ F J,BERNAL J,SNCHEZ-MONTES C,et al.Bright spot regions segmentation and classification for specular highlights detection in colonoscopy videos[J].Machine vision and applications,2017,28(8):917-936.
[7]BRUIJN M P,VAN DER LINDEN A J,RIDDER ML,et al.FDM readout assembly with flexible,superconducting connection to cryogenic kilo-Pixel TES detectors[J].Journal of low temperature physics,2016,184(1/2):369-373.
[8]韩思奇,王蕾.图像分割的阈值法综述[J].系统工程与电子技术,2002,24(6):91-94.
[9]HUO F C,LIU Y,WANG D,et al.Bloch quantum artificial bee colony algorithm and its application in image threshold segmentation[J].Signal,image and video processing,2017,11(8):1585-1592.
[10]吴一全,孟天亮,吴诗婳.图像阈值分割方法研究进展20年(1994-2014)[J].数据采集与处理,2015,30(1):1-23.
[11]王俊英,颜芬芬,陈鹏,等.基于概率自适应蚁群算法的云任务调度方法[J].郑州大学学报(工学版),2017,38(4):51-56.
[12]曹爽,安建成.狼群优化的二维Otsu快速图像分割算法[J].计算机工程与科学,2018,40(7):1221-1226.
[13]段海滨,叶飞.鸽群优化算法研究进展[J].北京工业大学学报,2017,43(1):1-7.
[14]杨之元,段海滨,范彦铭.基于莱维飞行鸽群优化的仿雁群无人机编队控制器设计[J].中国科学(技术科学),2018,48(2):161-169.
[15]单鑫,王艳,纪志成.基于参数知识鸽群算法的离散车间能效优化[J].系统仿真学报,2017,29(9):2140-2149.
[16]DEHSHIBI M M,SOURIZAEI M,FAZLALI M,et al.A hybrid bio-inspired learning algorithm for image segmentation using multilevel thresholding[J].Multimedia tools and applications,2017,76(14):15951-15986.
[17]HAKLI H,UGUZ H.A novel particle swarm optimization algorithm with Levy flight[J].Applied soft computing,2014,23:333-345.
[18]曹静,王元,娄泽坤,等.基于链式竞争遗传算法的KSW熵的图像分割[J].传感器与微系统,2017,36(11):128-130.
[2]杨进,郑允,马良.改进的猫群算法求解TSP[J].计算机应用研究,2017,34(12):3607-3610.
[3]LONG W,LIANG X M,CAI S H,et al.A modified augmented Lagrangian with improved grey wolf optimization to constrained optimization problems[J].Neural computing and applications,2017,28(S1):421-438.
[4]张军国,冯文钊,胡春鹤,等.无人机航拍林业虫害图像分割复合梯度分水岭算法[J].农业工程学报,2017,33(14):93-99.
[5]LIANG Y F,SUN L,SER W,et al.Hybrid threshold optimization between global image and local regions in image segmentation for melasma severity assessment[J].Multidimensional systems and signal processing,2017,28(3):977-994.
[6]SNCHEZ F J,BERNAL J,SNCHEZ-MONTES C,et al.Bright spot regions segmentation and classification for specular highlights detection in colonoscopy videos[J].Machine vision and applications,2017,28(8):917-936.
[7]BRUIJN M P,VAN DER LINDEN A J,RIDDER ML,et al.FDM readout assembly with flexible,superconducting connection to cryogenic kilo-Pixel TES detectors[J].Journal of low temperature physics,2016,184(1/2):369-373.
[8]韩思奇,王蕾.图像分割的阈值法综述[J].系统工程与电子技术,2002,24(6):91-94.
[9]HUO F C,LIU Y,WANG D,et al.Bloch quantum artificial bee colony algorithm and its application in image threshold segmentation[J].Signal,image and video processing,2017,11(8):1585-1592.
[10]吴一全,孟天亮,吴诗婳.图像阈值分割方法研究进展20年(1994-2014)[J].数据采集与处理,2015,30(1):1-23.
[11]王俊英,颜芬芬,陈鹏,等.基于概率自适应蚁群算法的云任务调度方法[J].郑州大学学报(工学版),2017,38(4):51-56.
[12]曹爽,安建成.狼群优化的二维Otsu快速图像分割算法[J].计算机工程与科学,2018,40(7):1221-1226.
[13]段海滨,叶飞.鸽群优化算法研究进展[J].北京工业大学学报,2017,43(1):1-7.
[14]杨之元,段海滨,范彦铭.基于莱维飞行鸽群优化的仿雁群无人机编队控制器设计[J].中国科学(技术科学),2018,48(2):161-169.
[15]单鑫,王艳,纪志成.基于参数知识鸽群算法的离散车间能效优化[J].系统仿真学报,2017,29(9):2140-2149.
[16]DEHSHIBI M M,SOURIZAEI M,FAZLALI M,et al.A hybrid bio-inspired learning algorithm for image segmentation using multilevel thresholding[J].Multimedia tools and applications,2017,76(14):15951-15986.
[17]HAKLI H,UGUZ H.A novel particle swarm optimization algorithm with Levy flight[J].Applied soft computing,2014,23:333-345.
[18]曹静,王元,娄泽坤,等.基于链式竞争遗传算法的KSW熵的图像分割[J].传感器与微系统,2017,36(11):128-130.