基于AHLO与K均值聚类的图像分割算法
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摘要
针对图像分割中K均值算法全局搜索能力差、初始聚类中心选择敏感的问题,提出了一种将自适应人类优化算法与K均值算法相结合的聚类算法.该算法利用自适应人类学习优化算法初始化聚类中心,提高K均值算法的稳健性.结果表明,该算法聚类得到的标准差相比传统K均值算法和基于粒子群K均值(PSO-Kmeans)算法分别小两个数量级和一个数量级,同时图像分割得到的PSNR值均较高,具有算法收敛速度更快,聚类质量更好,图像分割效果更好,适应性更强的优点.
Aiming at the problem of poor global searching ability and the sensitivity of initial clustering center selection by K-means algorithm for image segmentation,a clustering algorithm with the combination of adaptive human learning optimization( AHLO) and K-means algorithms was proposed. AHLO algorithm was used to initialize the clustering centers in the proposed algorithm so as to improve the stability of K-means algorithm. The results showthat the standard deviation obtained with the proposed clustering algorithm is two orders of magnitude lower than the traditional K-means algorithm and one order of magnitude lower than the PSO-Kmeans algorithm,respectively. Meanwhile,the PSNR values of image segmentation obtained by the proposed algorithm are relatively higher. The as-proposed algorithm has the features of faster convergence speed,better clustering quality,better image segmentation effect and stronger adaptability.
Aiming at the problem of poor global searching ability and the sensitivity of initial clustering center selection by K-means algorithm for image segmentation,a clustering algorithm with the combination of adaptive human learning optimization( AHLO) and K-means algorithms was proposed. AHLO algorithm was used to initialize the clustering centers in the proposed algorithm so as to improve the stability of K-means algorithm. The results showthat the standard deviation obtained with the proposed clustering algorithm is two orders of magnitude lower than the traditional K-means algorithm and one order of magnitude lower than the PSO-Kmeans algorithm,respectively. Meanwhile,the PSNR values of image segmentation obtained by the proposed algorithm are relatively higher. The as-proposed algorithm has the features of faster convergence speed,better clustering quality,better image segmentation effect and stronger adaptability.
引文
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[2]马英辉,吴一全.基于二维Renyi交叉熵的刀具磨损图像分割[J].电子测量与仪器学报,2016,30(12):1869-1876.(MA Ying-hui,WU Yi-quan. Image segmentation for tool w ear based on 2D Renyi cross entropy[J]. Journal of Electronic M easurement and Instrument,2016,30(12):1869-1876.)
[3]夏平,刘小妹,雷帮军,等.基于复小波域树结构化M RF模型的声纳图像分割[J].仪器仪表学报,2016,37(4):895-903.(XIA Ping,LIU Xiao-mei,LEI Bang-jun,et al. Sonar image segmentation based on tree-structured M RF model in complex-w avelet domain[J]. Chinese Journal of Scientific Instrument,2016,37(4):895-903.)
[4]刘琼,史诺,申妙芳.基于区间二型模糊集的农田障碍物分割方法[J].国外电子测量技术,2016,35(4):81-84.(LIU Qiong,SHI Nuo,SHEN Miao-fang. Farmland obstacle image segmentation based on interval typeⅡfuzzy set[J]. Foreign Electronic Measurement Technology,2016,35(4):81-84.)
[5]王云烨,李勃,董蓉,等.基于透射率空间与色彩纹理相关性的图像分割[J].电子测量技术,2015,38(1):41-46.(WANG Yun-ye,LI Bo,DONG Rong,et al. An transmittance space and color-texture correlation based graph theoretic image segmentation method[J]. Electronic M easurement Technology,2015,38(1):41-46.)
[6]刘琼,史诺.基于Lab和YUV颜色空间的农田图像分割方法[J].国外电子测量技术,2015,34(4):39-41.(LIU Qiong,SHI Nuo. Farmland image segmentation based on Lab and YUV color spaces[J]. Foreign Electronic M easurement Technology,2015,34(4):39-41.)
[7] Moftah H M,Azar A T,Al-Shammari E T,et al.Adaptive K-means clustering algorithm for M R breast image segmentation[J]. Neural Computing and Applications,2014,24(7/8):1917-1928.
[8] Jumb V,Sohani M,Shrivas A. Color image segmentation using K-means clustering and Otsu’s adaptive thresholding[J]. International Journal of Innovative Technology and Exploring Engineering,2014,3(9):72-76.
[9]徐黎明,吕继东.基于同态滤波和K均值聚类算法的杨梅图像分割[J].农业工程学报,2015,31(14):202-208.(XU Li-ming,LJi-dong. Bayberry image segmentation based on homomorphic filtering and K-means clustering algorithm[J]. Transactions of the Chinese Society of Agricultural Engineering,2015,31(14):202-208.)
[10]Dhanachandra N,Manglem K,Chanu Y J. Image segmentation using K-means clustering algorithm and subtractive clustering algorithm[J]. Procedia Computer Science,2015,54:764-771.
[11]Velmurugan T. Performance based analysis between K-means and fuzzy C-means clustering algorithms for connection oriented telecommunication data[J].Applied Soft Computing,2014,19:134-146.
[12]Tzortzis G,Likas A. The minmax K-means clustering algorithm[J]. Pattern Recognition,2014,47(7):2505-2516.
[13]Li H,He H,Wen Y. 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.
[14]Younus Z S,Mohamad D,Saba T,et al. Contentbased image retrieval using PSO and K-means clustering algorithm[J]. Arabian Journal of Geosciences,2015,8(8):6211-6224.
[15]Wang L,Ni H Q,Yang R,et al. A simple human learning optimization algorithm[C]//International Conference on Life System M odeling and Simulation and International Conference on Intelligent Computing for Sustainable Energy and Environment. Shanghai,China,2014:56-65.
[16]Wang L,Yang R,Ni H,et al. A human learning optimization algorithm and its application to multidimensional knapsack problems[J]. Applied Soft Computing,2015,34:736-743.
[17]Wang L,Ni H,Yang R,et al. An adaptive simplified human learning optimization algorithm[J]. Information Sciences,2015,320:126-139.