计算机科学 ›› 2017, Vol. 44 ›› Issue (6): 278-282.doi: 10.11896/j.issn.1002-137X.2017.06.049
朱春,李林国,郭剑
ZHU Chun, LI Lin-guo and GUO Jian
摘要： 模糊C均值聚类算法(FCM)是一种应用非常广泛的聚类算法,但是它受初始聚类中心影响较大,容易陷入局部最优。 在标准布谷鸟算法(CS)的基础上 提出改进布谷鸟优化算法(ICS),将发现概率P由固定值转变成随迭代次数逐渐减小的变量,这样不仅可以提高搜索种群的质量,而且保证了算法的收敛。因此,可以将改进布谷鸟优化算法用于FCM算法聚类中心生成的过程(ICS_FCM),从而有效地避免FCM陷入局部最优。改进的算法具有良好的聚类效果和运行速度。实现基于改进布谷鸟优化的FCM图像分割,并与基于模拟退火的FCM算法(SA_FCM)进行对比。由实验结果可知,该算法(ICS_FCM)不仅取得了较好的分割效果,效率上也有明显的提高。
| [1] TANG J.A Color Image Segmentation algorithm Based on Region Growing[C]∥The 2nd International Conference on Computer Engineering and Technology.Chengdu,2010:334-337. [2] GENG Y Z,CHEN J ,WANG L.A Novel Color Image Segmentation Algorithm Based on JSEG and Normalized Cuts[C]∥The 6nd International Congress on Image and Signal Processing.Hangzhou,2013:550-554. [3] MEURIE C,RUICHEK Y,SBIHI A,et al.Watershed regions and watershed lines based cooperation strategy for image segmentation.Application to roof detection[C]∥IEEE Internatio-nal Symposium on Signal Processing and Information Technology.Bilbao,2013:393-398. [4] MOFFETT K B,GOREICK S M.Distinguishing wetland vegetation and channel features with object-based image segmentation[J].International Journal of Remote Sensing,2011,4(4):1332-1354. [5] SANCHEZ JMARTINEZ E A.Arquero.Improving Parameters Selection of a Seeded Region Growing Method for Multiband Ima-ge Segmentation[J].IEEE Latin America Transactions,2015,3(3):843-849. [6] JORDILNGLADA P L,MICHEL J.A Scalable Tile-Based Fra-mework for Region-Merging Segmentation[J].IEEE Transactions on Geoscience and Remote Science,2015,3(10):5473-5485. [7] WU K H,BAN T.Optimal Threshold Image Segmentation Me-thod Based on Genetic Algorithm in Wheel Set Online Measurement[C]∥Third International Conference on Measuring Technology and Mechatronics Automation.Shanghai,2011:799-802. [8] XU X Y,ZHANG Z J,XIE J,et al.Threshold Image Segmentation Based on Granular Immune Algorithm[C]∥Control and Decision Conference.Guilin,2009:3512-3515. [9] LIN K P.A Novel Evolutionary Kernel Intuitionistic Fuzzy C-means Clustering Algorithm[J].IEEE Transactions on Fuzzy Systems,2014,2(5):1074-1087. [10] ZHONG Y F,MA A L,ZHANG L P.An Adaptive Memetic Fuz-zy Clustering Algorithm With SpatialInformation for Remote SensingImagery[J].IEEE Selected Topics in Applied Earth Observations and Remote Sensing,2014,7(4):1235-1248. [11] YANG Y F,WANG Y P.Simulated annealing spectral clustering algorithm for image segmentation[J].Journal of Systems Engineering and Electronics,2014,5(3):514-522. [12] YANG Q,WANG Z Q.Fuzzy c-means image segmentation algorithm based on chaotic simulated annealing[C]∥International Conference on Mechatronics,Materials and Manufacturing.Chengdu,2014:536-539. [13] RICHARD J H,JAMES C B.Extending Fuzzy and Probabilistic Clustering to Very Large Data Sets[J].Journal of Computatio-nal Statistics and Data Analysis,2006,1(1):215-234. [14] YANG XS.Cuckoo search via Levy flights[C]∥World Congress on IEEE.2009:210-214. [15] LIU X N.Application of cuckoo search algorithm in multi-th- reshold image segmentation[J].Computer Engineering,2013,9(7):274-278. [16] YANG X S.Muitiobjective cuckoo search for design optimization[J].Computers & Operations Research,2013,0(6):1616-1624. [17] PAVLY.Levy,flights,non-local search and simulated annealing[J].Journal of Computational Physics,2007,6(2):1830-1844. [18] EID E,HOSSAM M.Retinal Vessel Segmentation based on Pos-sibilistic Fuzzy c-means Clustering Optimised with Cuckoo Search[C]∥Intertional Joint Conference on Neural NetWorks.Beijing,2014:1792-1796. [19] HE X,ZEMEL R S,RAY D.Learning and incorporating top-down cues in image segmentation[M]∥Computer Vision-ECCV2006.Springer Berlin Heidelberg,2006:338-351. |
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