基于杜鹃搜索和二维Fisher准则的图像分割方法
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摘要
阈值法是图像分割最为常用的方法之一,然而基于一维直方图的阈值方法分割结果容易受噪声的影响.基于二维直方图的二维Fisher准则能够克服一维阈值法缺陷,具有较好的分割性能.但是二维Fisher准则阈值法在求取最优阈值时需要大量的计算,运算速度非常慢.常用的二维Fisher准则阈值优化计算方法如粒子群算法和遗传算法容易陷入局部最优.杜鹃搜索算法是新近提出的一种元启发优化算法,一些经典的函数优化问题测试结果表明杜鹃搜索算法全局寻优能力优于粒子群算法和遗传算法.在介绍杜鹃搜索算法的基础上,提出一种基于杜鹃搜索算法改进的二维Fisher准则阈值分割方法.实验结果证明,提出的方法降低了基本二维Fisher准则阈值法最优阈值的寻找时间,提高了图像分割的实时性,是一种性能良好的图像分割方法.
Thresholding method is one of the most common methods for image segmentation,however thresholding methods based on 1-D histogram are easily ruined by the noise. Thresholding based on 2-D histogram and Fisher criterion function can overcome the shortcoming of 1-D threshold method,which has the better segmentation performance. But due to huge computation is required for 2-D Fisher criterion function its speed is very slow. Commonly used optimization methods to speed up thresholding based on 2-D Fisher criterion function like particle swarm optimization and genetic algorithm are easily to fall into the local optimum. Cuckoo search is a newly proposed meta-heuristic optimization algorithm; testing results on some benchmarks indicate that cuckoo search has better global convergence ability than particle swarm optimization and genetic algorithm.In this paper,by employing cuckoo search algorithm,a segmentation approach was proposed based on 2-D Fisher criterion function. The experimental results show that the proposed method decreases the seeking time of optimal threshold with the basic 2-D Fisher criterion based thresholding method,which is a well performing method and is more suitable for real-time image segmentation.
Thresholding method is one of the most common methods for image segmentation,however thresholding methods based on 1-D histogram are easily ruined by the noise. Thresholding based on 2-D histogram and Fisher criterion function can overcome the shortcoming of 1-D threshold method,which has the better segmentation performance. But due to huge computation is required for 2-D Fisher criterion function its speed is very slow. Commonly used optimization methods to speed up thresholding based on 2-D Fisher criterion function like particle swarm optimization and genetic algorithm are easily to fall into the local optimum. Cuckoo search is a newly proposed meta-heuristic optimization algorithm; testing results on some benchmarks indicate that cuckoo search has better global convergence ability than particle swarm optimization and genetic algorithm.In this paper,by employing cuckoo search algorithm,a segmentation approach was proposed based on 2-D Fisher criterion function. The experimental results show that the proposed method decreases the seeking time of optimal threshold with the basic 2-D Fisher criterion based thresholding method,which is a well performing method and is more suitable for real-time image segmentation.
引文
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[2]陈果.图像阈值分割的Fisher准则函数法[J].仪器仪表学报,2003,24(6):564-567.
[3]吴谨,李娟,刘成云,等.基于最大熵的灰度阈值选取方法[J].武汉科技大学学报(自然科学版),2004,27(1):58
[4]刘申晓,王学春,常朝稳.基于改进粒子群优化算法的Otsu图像分割方法[J].计算机科学,2013,40(8):293-295.
[5]Zhang J,Hu J L.Image segmentation based on 2D otsu method with histogram analysis[C]//Proceedings of The 2008International Conference on Computer Science and Software Engineering,Wuhan,China,2008,11:105-108.
[6]童莹,邱晓晖.基于Fisher准则函数的二维阈值图像分割算法[J].电力系统通信,2004,9:36-39.
[7]欧萍,贺电.遗传算法粒在二维最大熵值图像分割中的应用[J].计算机仿真,2011,28(1):294-297.
[8]黄港,李俊,潘金贵.基于粒子群优化方法的2维Otsu快速图像分割算法[J].中国图象图形学报,2011,6(3):377-381.
[9]唐英干,黄娜,关新平.基于二维Fisher线性鉴别分析和粒子群优化的红外图像分割[J].电子器件,2009,32(1):13-16.
[10]Li L Y,Li D R.Fuzzy entropy image segmentation based on particle swarm optimization[J].Progress in Natural Science,2008,18(9):1167-1171.
[11]Ye Z W,Zhou X,Hu Z B.Image segmentation based on 2-D fisher and chaos particle swarm optimization algorithm[C]//2010 2ndInternational Symposium on Information Engineering and Electronic Commerce.Ternopil,Ukraine:IEEE Publications,2010.143-146.
[12]温淑慧,刘夏.基于正交试验的PSO-Fisher图像分割算法[J].激光与红外,2008,38(11):1168-1172.
[13]Ye Z W,Li Q Y,Zeng M D.Image segmentation using thresholding and artificial fish-swarm algorithm[C]//2012 The 2nd International Conference on Computer Science and Service System.Nanjing,China:IEEE Publications,2012.1054-1057.
[14]Yang X S,Deb S.Engineering optimization by cuckoo search[J].International Journal of Mathematical Modelling and Numerical Optimisation.2010,1(4):330-343.
[15]Yang X S,Deb S.Cuckoo search via Levy flights[C]//Proceeding of World Congress on Nature and Biologically Inspired Computing.India,USA:IEEE Publications,2009.210-214.
[16]Yang X S.Cuckoo search for inverse problems and simulated-driven shape optimization[J].Journal of Computational Methods in Sciences and Engineering,2012,12:129-137.
[17]Civicioglu P,Erkan B.A conceptual comparison of the Cuckoo-search,particle swarm optimization,differential evolution and artificial bee colony algorithms[J].Artificial Intelligence Review,2013,39(4):315-346.
[18]李煜,马良.新型元启发式布谷鸟搜索算法[J].系统工程.2012,30(8):64-69.
[19]Mantegna R N.Fast accurate algorithm for numerical simulation of levy stable stochastic processes[J].Physical Review E,1994,5(49):4677-4683.
[20]Ye Z W,Hu Z B,Lai X D,et al.Image segmentation using thresholding and swarm intelligence[J].Journal of Software,2012,7(5):1074-1082.