二维熵图像分割方法利用图像的局部空间信息,分割结果优于一维熵分割法,但计算效率较低.遗传算法和粒子群算法等最优化算法能提高二维熵图像分割方法的效率,却不能保证获得全局最优阈值.为此,提出一种基于差分进化算法的二维熵图像阈值分割法,用局部搜索策略提高搜索最优阈值的精度.实验表明,所提出的方法有良好的鲁棒性,能保证获得最优阈值.与基本的二维熵图像分割法相比,分割速度有很大的提高.
Image segmentation based on 2-D entropy uses local space information of images and has better segmentation results than 1-D entropy based methods, but the computation efficiency is low. Commonly used optimization algorithms such as genetic algorithm and particle swarm optimization can improve efficiency of 2-D entropy thresholding, but cannot ensure the optimal threshold values. In the present paper, an image segmentation approach based on differential evolution and 2-D entropy is proposed to avoid drawbacks of the above methods. A local search strategy is used to further improve precision of the optimal threshold. Experimental results indicate that the proposed method is robust, and can acquire the optimal threshold values with much faster running speed than the primary 2-D entropy thresholding method.
[1] Chandhok C, Chaturvedi S, Khurshid A A. An approach to image segmentation using K-means clustering algorithm[J]. International Journal of Information Technology, 2012, 1(1):11-17.
[2] 周开军,桂卫华,阳春华,谢永芳. 基于模糊三值模式的矿物浮选泡沫图像边缘检测方法[J]. 电子学报,2014, 42(4):658-665.Zhou K J, Gui W H, Yang C H, Xie Y F. Mineral floatation froth image edge detection method based on fuzzy ternary pattern[J]. Acta Electronica Sinica, 2014, 42(4):658-665. (in Chinese)
[3] Waseem K. Image segmentation techniques:a survey[J]. Journal of Image and Graphics, 2013, 1(4):166-120.
[4] 何志勇,孙立宁,芮延年. 一种微小表面缺陷的机器视觉检测方法[J]. 应用科学学报,2012, 30(5):531-537. He Z Y, Sun L N, Rui Y N. Detection of small surface defects based on machine vision[J]. Journal of Applied Sciences, 2012, 30(5):531-537. (in Chinese)
[5] 陈洪,陶超,邹峥嵘,于菲菲. 一种新的高分辨率遥感影像城区提取方法[J]. 武汉大学学报:信息科学版,2013, 38(9):1063-1067. Chen H, Tao C, Zou Z R, Yu F F. Automatic urban area extraction using a Gabor filter and high-resolution remote sensing imagery[J]. Geomatics and Information Science of Wuhan University, 2013, 38(9):1063-1067. (in Chinese)
[6] 吴一全,曹鹏祥,王凯,殷骏. 二维Arimoto灰度熵阈值分割[J]. 应用科学学报,2014, 32(4):331-334. Wu Y Q, Cao P X, Wang K, Yin J. Two-dimensional Arimoto gray entropy thresholding[J]. Journal of Applied Sciences, 2014, 32(4):331-334. (in Chinese)
[7] 张新明,冯文惠,何文涛,王鲜芳. 基于人工蜂群算法的二维最小误差阈值分割[J]. 广西大学学报:自然科学版,2013, 38(5):1126-1133. Zhang X M, Feng W H, He W T, Wang X F. Two-dimensional minimum error thresholding method based on the artificial bee colony algorithm[J]. Journal of Guangxi University:Natural Science Edition, 2013, 38(5):1126-1133. (in Chinese)
[8] 张新明,孙印杰,郑延斌. 二维直方图准分的Otsu图像分割及其快速实现[J]. 电子学报,2011, 39(8):1778-1784. Zhang X M, Sun Y J, Zheng Y B. Precise two-dimensional Otsu's image segmentation and its fast recursive realization[J]. Acta Electronica Sinica, 2011, 39(8):1778-1784. (in Chinese)
[9] Ye Z W, Hu Z B, Lai X D, Chen H W. Image segmentation using thresholding and swarm intelligence[J]. Journal of Software, 2012, 7(5):1074-1082.
[10] 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):294-298. Ou P, He D. 2-D maximum entropy method of image segmentation based on genetic algorithm[J]. Computer Simulation, 2011, 28(1):294-298. (in Chinese)
[11] 张新娟,雷秀娟. 改进PSO算法在二维最佳阈值图像分割中的应用[J]. 计算机工程与应用,2011, 47(26):207-209. Zhang X J, Lei X J. Application of improved PSO algorithm on two dimension best threshold image segmentation[J]. Computer Engineering and Applications, 2011, 47(26):207-209. (in Chinese)
[12] 丁贤云,朱煜. 基于二维熵的人工鱼群材料图像分割方法[J]. 激光与红外,2012, 40(2):210-214. Ding X Y, Zhu Y. Segmentation for sem image based on two-dimensional entropy and artificial fish swarm algorithm[J]. Laser & Infrared, 2012, 40(2):210-214. (in Chinese)
[13] Storn R, Price K. Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces[J]. Journal of Global Optimization, 1997, 11:341-359.
[14] 杨启文,蔡亮,薛云灿. 差分进化算法综述[J]. 模式识别与人工智能,2008, 21(4):506-513. Yang Q W, Cai L, Xue Y C. A survey of differential evolution algorithms[J]. Pattem Recognition and Artificial Intelligence, 2008, 21(4):506-513. (in Chinese)
[15] 张英杰,龚中汉,陈乾坤. 基于免疫离散差分进化算法的复杂网络社区发现[J]. 自动化学报,2015, 41(4):749-756. Zhang Y J, Gong Z H, Chen Q K. Community detection in complex networks using immune discrete differential evolution algorithm[J]. Aata Automatica Sinica, 2015, 41(4):749-756. (in Chinese)
[16] Soham S, Swagatam D. Multilevel image thresholding based on 2D histogram and maximum Tsallis entropy-a differential evolution approach[J]. IEEE Transactions on Image Processing, 2012, 22(12):4788-4797.
