基于复合布谷鸟算法的彩色图像多阈值分割
|
摘要
彩色图像多阈值分割是图像处理中的难点。将人工蜂群算法(ABC)中蜜蜂的寻优方程引入布谷鸟搜索(CS)算法中的莱维飞行结束后对布谷鸟的位置进行变异,并对布谷鸟算法中的步长因子和发现概率各引入一个新的非线性递减方程,在此基础上形成了一种复合布谷鸟算法(HCS),并以此复合布谷鸟算法(HCS)进行彩色图像多阈值分割,与CS算法、PSO算法分别作用于彩色图像多阈值分割进行对比实验,实验结果表明,论文提出的HCS算法无论从彩色图像多阈值分割的主观效果还是客观效果,在这三种算法中都是最好的。
Multi threshold segmentation of color image is a difficult problem in image processing. After the end of the Levi flight for Cuckoo search(CS) algorithm,the optimization equation of the bees in An artificial bee colony algorithm(ABC) are introduced,the position of the cuckoo is changed. The step size factor and the discovery probability are introduced into a new nonlinear decreasing equation in CS,based on this,a hybrid cuckoo algorithm(HCS) is formed then the HCS algorithm is used to make multi threshold segmentation of color images,so do CS algorithm and PSO algorithm. Experimental results show that,the HCS algorithm proposed in this paper is the best of both the subjective and objective effects of multi threshold segmentation of color image in the three algorithms.
Multi threshold segmentation of color image is a difficult problem in image processing. After the end of the Levi flight for Cuckoo search(CS) algorithm,the optimization equation of the bees in An artificial bee colony algorithm(ABC) are introduced,the position of the cuckoo is changed. The step size factor and the discovery probability are introduced into a new nonlinear decreasing equation in CS,based on this,a hybrid cuckoo algorithm(HCS) is formed then the HCS algorithm is used to make multi threshold segmentation of color images,so do CS algorithm and PSO algorithm. Experimental results show that,the HCS algorithm proposed in this paper is the best of both the subjective and objective effects of multi threshold segmentation of color image in the three algorithms.
引文
[1]Manikandan,S.,Ramar,K.,Willjuice,I.M.,&Srinivasagan,K.G.Multilevel thresholding for segmentation of medical brain images using real coded genetic algorithm[J].Measurement,2014,47:558-568.
[2]D.-J.Liu,H.-J.Wang,S.Wang etc.Quaternion-based improved artificial bee colony algorithm for color remote sensing image edge detection[J].Mathematical Problems in Engineering,2015(3):138930-138939.
[3]Tian Zhenkun,Fu Yingying,Liu Suhong,et al.Rapid crops classification based on UAV low-altitude remote sensing[J].Transactions of the Chinese Society of Agricultural Engineering(Transation of the CSAE),2013,29(7):109-116.
[4]Malek,Salim,Bazi,Yakoub,Alajlan,Naif et al.Efficient Framework for Palm Tree Detection in UAV Images[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2014,7(12):4692-4703.
[5]Tian Zhenkun,Fu Yingying,Liu Suhong,et al.Rapid crops classification based on UAV low-altitude remote sensing[J].Transactions of the Chinese Society of Agricultural Engineering(Transation of the CSAE),2013,29(7):109-116.
[6]Dey,S.,Saha,I.,Bhattacharyya,S.,&Maulik,U.Multi-level thresholding using quantum inspired meta-heuristics[J].Knowledge-Based Systems,2014,67,373-400.
[7]Dirami,A.,Hammouche,K.,Diaf,M.,&Siarry,P.Fast multilevel thresholding for image segmentation through a multiphase level set method[J].Signal Processing,2013,93(1):139-153.
[8]Kurban,T.,Civicioglu,P.,Kurban,R.,&Besdok,E..Comparison of evolutionary and swarm based on computational techniques for multilevel color image thresholding[J].Applied Soft Computing,2014,23:128-143.
[9]LIU,Y.,MU,C.,KOU,W.,&LIU,J.Modified particle swarm optimization-based multilevel thresholding for image segmentation[J].Soft Computing,2014:1-17.
[10]Pérez-Ortiz,María,Gutiérrez,Pedro Antonio,Pe?a,Jose Manuel et al.Unmanned aerial vehicles produce high-resolution,seasonally-relevant imagery for classifying wetland vegetation[J].International Archives of the Photogrammetry,Remote Sensing and Spatial Information Sciences ISPRS Archives,2015(40):249-256.
[11]Tian Zhenkun,Fu Yingying,Liu Suhong,et al.Rapid crops classification based on UAV low-altitude remote sensing[J].Transactions of the Chinese Society of Agricultural Engineering(Transation of the CSAE),2013,29(7):109-116.
[12]J.Tomes-Sanchez,F.Lopez-Granados,J.M.Pena.An automatic object-based method for optimal thresholding in UAV images:Application for vegetation detection in herbaceous crops[J].Computers and Electronics in Agriculture 2015(114):43-52.
[13]Marcin Wozniak,Dawid Polap,Christian Napoli.Graphic object feature extraction system based on Cuckoo Search Algorithm[J].Expert Systems With Applications,2016(66):20-31.
[14]David A.Wood.Hybrid cuckoo search optimization algorithms applied to complex wellbore trajectories aided by dynamic,chaos-enhanced,fat-tailed distribution sampling and metaheuristic profiling[J].journal of Natural Gas Science and Engineering,2016(34):236-252.
[15]VALIAN E,MOHANNA S,TAVAKOLI S.Improved Cuckoo search algorithm for global optimization[J].Int.J.Comunications and Information Technology,2014,1(1):31-34.
[2]D.-J.Liu,H.-J.Wang,S.Wang etc.Quaternion-based improved artificial bee colony algorithm for color remote sensing image edge detection[J].Mathematical Problems in Engineering,2015(3):138930-138939.
[3]Tian Zhenkun,Fu Yingying,Liu Suhong,et al.Rapid crops classification based on UAV low-altitude remote sensing[J].Transactions of the Chinese Society of Agricultural Engineering(Transation of the CSAE),2013,29(7):109-116.
[4]Malek,Salim,Bazi,Yakoub,Alajlan,Naif et al.Efficient Framework for Palm Tree Detection in UAV Images[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2014,7(12):4692-4703.
[5]Tian Zhenkun,Fu Yingying,Liu Suhong,et al.Rapid crops classification based on UAV low-altitude remote sensing[J].Transactions of the Chinese Society of Agricultural Engineering(Transation of the CSAE),2013,29(7):109-116.
[6]Dey,S.,Saha,I.,Bhattacharyya,S.,&Maulik,U.Multi-level thresholding using quantum inspired meta-heuristics[J].Knowledge-Based Systems,2014,67,373-400.
[7]Dirami,A.,Hammouche,K.,Diaf,M.,&Siarry,P.Fast multilevel thresholding for image segmentation through a multiphase level set method[J].Signal Processing,2013,93(1):139-153.
[8]Kurban,T.,Civicioglu,P.,Kurban,R.,&Besdok,E..Comparison of evolutionary and swarm based on computational techniques for multilevel color image thresholding[J].Applied Soft Computing,2014,23:128-143.
[9]LIU,Y.,MU,C.,KOU,W.,&LIU,J.Modified particle swarm optimization-based multilevel thresholding for image segmentation[J].Soft Computing,2014:1-17.
[10]Pérez-Ortiz,María,Gutiérrez,Pedro Antonio,Pe?a,Jose Manuel et al.Unmanned aerial vehicles produce high-resolution,seasonally-relevant imagery for classifying wetland vegetation[J].International Archives of the Photogrammetry,Remote Sensing and Spatial Information Sciences ISPRS Archives,2015(40):249-256.
[11]Tian Zhenkun,Fu Yingying,Liu Suhong,et al.Rapid crops classification based on UAV low-altitude remote sensing[J].Transactions of the Chinese Society of Agricultural Engineering(Transation of the CSAE),2013,29(7):109-116.
[12]J.Tomes-Sanchez,F.Lopez-Granados,J.M.Pena.An automatic object-based method for optimal thresholding in UAV images:Application for vegetation detection in herbaceous crops[J].Computers and Electronics in Agriculture 2015(114):43-52.
[13]Marcin Wozniak,Dawid Polap,Christian Napoli.Graphic object feature extraction system based on Cuckoo Search Algorithm[J].Expert Systems With Applications,2016(66):20-31.
[14]David A.Wood.Hybrid cuckoo search optimization algorithms applied to complex wellbore trajectories aided by dynamic,chaos-enhanced,fat-tailed distribution sampling and metaheuristic profiling[J].journal of Natural Gas Science and Engineering,2016(34):236-252.
[15]VALIAN E,MOHANNA S,TAVAKOLI S.Improved Cuckoo search algorithm for global optimization[J].Int.J.Comunications and Information Technology,2014,1(1):31-34.