液晶与显示, 2017, 32 (9): 726, 网络出版: 2017-10-30   

融合改进人工蜂群和K均值聚类的图像分割

Image segmentation algorithm based on improved artificial bee colony and K-mean clustering
作者单位
大理大学 工程学院, 云南 大理 671003
摘要
针对人工蜂群优化的K均值算法易陷入局部最优、搜索精度不够、分割图像不够细致等问题, 本文融合自适应人工蜂群和K均值聚类, 提出了一种新的图像分割算法。算法首先利用距离最大最小乘积对种群进行初始化;其次采用自适应搜索参数动态调整邻域搜索范围, 使人工蜂群算法快速收敛于全局最优;然后将人工蜂群输出的所有蜜源进行K均值聚类, 克服K均值聚类结果对初始聚类中心的依赖, 再将聚类划分结果进行Powell局部搜索, 加快算法收敛的速度, 将得到的新聚类中心更新蜂群中蜜源位置。最后, 将本文算法与其他两种同类分割算法进行试验对比。实验结果表明:与其他两种算法相比, 本文提出的分割算法在保证运行时间的前提下, 分割准确率比其他两种算法分别至少提高了3.5%和4.8%, 表现出了较高的分割质量。
Abstract
In order to overcome the artificial colony optimization k-means which be fallen into local optimum easily, converged slowly, segmented roughly and other issues, a new image segmentation algorithm is proposed based on adaptive artificial bee colony and K-mean clustering. First, the population is initialized by the maximum and minimum product; Secondly,adaptive search parameters are used to adjust neighborhood search scope dynamically,that makes artificial bee colony algorithm quickly converge to global optimal and achieve a more optimal solution; Then,all nectaries will be clustered by K-mean to the dependence of clustering result on the initial center, and then clustering results are divided into Powell local search, which accelerate the algorithm convergence speed, that will receive a new clustering center update colony of nectar source location. Finally, the proposed algorithm is compared with the other two algorithms. The experimental results show that compared with the other two algorithms, the segmentation algorithm proposed in this paper can improve the segmentation accuracy by at least 3.5% and 4.8%, respectively, under the premise of guaranteeing the running time, showing a higher segmentation quality.
参考文献

[1] SONKA M, HLAVAC V, BOYLE R, et al. Image processing, analysis and machine vision [J]. Journal of Electronic Imaging, 1996, 5(3): 423.

[2] MACQUEEN J. Some methods for classification and analysis of multivariate observations [C]//Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability. Berkeley: University of California Press, 1967: 281-297.

[3] ISA N A M, SALAMAH S A, NGAH U K. Adaptive fuzzy moving K-means clustering algorithm for image segmentation [J]. IEEE Transactions on Consumer Electronics, 2009, 55(4): 2145-2153.

[4] KARABOGA D, BASTURK B. A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm [J]. Journal of Global Optimization, 2007, 39(3): 459-471.

[5] KARABOGA D, AKAY B. A modified artificial bee colony (ABC) algorithm for constrained optimization problems [J]. Applied Soft Computing, 2011, 11(3): 3021-3031.

[6] 柳景青, 郭东进, 叶萍. 改进的给水管网节点K均值空间聚类[J]. 浙江大学学报(工学版), 2015, 49(11): 2128-2134.

    LIU J Q, GUO D J, YE P. Improved K average spatial clustering method for nodes of water distribution system [J]. Journal of Zhejiang University (Engineering Science), 2015, 49(11): 2128-2134. (in Chinese)

[7] LI H Y, HE H Z, WEN Y G. Dynamic particle swarm optimization and K-means clustering algorithm for image segmentation [J]. Optik-International Journal for Light and Electron Optics, 2015, 126(24): 4817-4822.

[8] 吴一全, 周杨, 龙云淋.基于蜂群优化投影寻踪的高光谱小目标检测[J].仪器仪表学报, 2016, 37(6):1347-1355.

    WU Y Q, ZHOU Y, LONG Y L. Detection of hyperspectral small targets based on projection pursuit optimized by bee colony [J]. Chinese Journal of Scientific Instrument, 2016, 37(6): 1347-1355. (in Chinese)

[9] 罗可, 李莲, 周博翔.一种蜜蜂交配优化聚类算法[J].电子学报, 2014, 42(12):2435-2441.

    LUO K, LI L, ZHOU B X. A Honey-bee mating optimization clustering algorithm [J]. Acta Electronica Sinica, 2014, 42(12): 2435-2441. (in Chinese)

[10] 喻金平, 郑杰, 梅宏标.基于改进人工蜂群算法的K均值聚类算法[J].计算机应用, 2014, 34(4):1065-1069, 1088.

    YU J P, ZHENG J, MEI H B. K-means clustering algorithm based on improved artificial bee colony algorithm [J]. Journal of Computer Applications, 2014, 34(4): 1065-1069, 1088. (in Chinese)

[11] KRISHNAVENI V, ARUMUGAM G. The performance analysis of a novel enhanced artificial bee colony inspired global best harmony search algorithm for clustering [C]//Proceedings of the International Conference on Information Systems Design and Intelligent Applications 2012 (INDIA 2012) held in Visakhapatnam, India. Berlin Heidelberg: Springer, 2012: 21-28.

[12] 曹永春, 蔡正琦, 邵亚斌.基于K-means的改进人工蜂群聚类算法[J].计算机应用, 2014, 34(1):204-207, 217.

    CAO Y C, CAI Z Q, SHAO Y B. Improved artificial bee colony clustering algorithm based on K-means [J]. Journal of Computer Applications, 2014, 34(1): 204-207, 217. (in Chinese)

[13] 柯钢, 杨俊.基于增强蜂群优化与K-means的文本聚类算法[J].计算机应用研究, 2016, 33(8):2298-2302.

    KE G, YANG J. Enhanced bee colony optimal and K-means based document clustering algorithm [J]. Application Research of Computers, 2016, 33(8): 2298-2302. (in Chinese)

[14] 毕晓君, 宫汝江.一种结合人工蜂群和K-均值的混合聚类算法[J].计算机应用研究, 2012, 29(6):2040-2042, 2046.

    BI X J, GONG R J. Hybrid clustering algorithm based on artificial bee colony and K-means algorithm [J]. Application Research of Computers, 2012, 29(6): 2040-2042, 2046. (in Chinese)

[15] 李莲, 罗可, 周博翔.一种改进人工蜂群的K-medoids聚类算法[J].计算机工程与应用, 2013, 49(16):146-150.

    LI L, LUO K, ZHOU B X. K-medoids clustering algorithm based on improved artificial bee colony [J]. Computer Engineering and Applications, 2013, 49(16): 146-150. (in Chinese)

[16] 熊忠阳, 陈若田, 张玉芳.一种有效的K-means聚类中心初始化方法[J].计算机应用研究, 2011, 28(11):4188-4190.

    XIONG Z Y, CHEN R T, ZHANG Y F. Effective method for cluster centers' initialization in K-means clustering [J]. Application Research of Computers, 2011, 28(11): 4188-4190. (in Chinese)

[17] 宋锦, 高浩, 王保云.改进人工蜂群算法在图像分割中的应用[J].电视技术, 2016, 40(8):8-14, 25.

    SONG J, GAO J, WANG B Y.Multilevel image segmentation based on improved artificial colony algorithm [J]. Video Engineering, 2016, 40(8): 8-14, 25. (in Chinese)

[18] 李海洋, 文永革, 何红洲, 等.基于随机权重粒子群和K-均值聚类的图像分割[J].图学学报, 2014, 35(5):755-761.

    LI H Y, WEN Y G, HE H Z, et al. An image segmentation algorithm based on random weight particle swarm optimization and K-means clustering [J]. Journal of Graphics, 2014, 35(5): 755-761. (in Chinese)

[19] 李彬, 刘同.基于D-S证据理论的多发性硬化症病灶分割算法[J].计算机应用研究, 2011, 28(1):378-380.

    LI B, LIU T. Segmentation of multiple sclerosis lesions based on D-S evidence theory [J]. Application Research of Computers, 2011, 28(1): 378-380. (in Chinese)

[20] WEI S, HONG Q, HOU MS. Automatic Image segmentation based on PCNN with adaptive threshold time constant [J]. Neurocomputing, 2011, 74(9): 1485-1491.

赵文昌, 李忠木. 融合改进人工蜂群和K均值聚类的图像分割[J]. 液晶与显示, 2017, 32(9): 726. ZHZO Wen-chang, LI Zhong-mu. Image segmentation algorithm based on improved artificial bee colony and K-mean clustering[J]. Chinese Journal of Liquid Crystals and Displays, 2017, 32(9): 726.

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