[1] [1] LOWE D G. Distinctive image features from scale-invariant keypoints [J]. International Journal of Computer Vision, 2004, 60(2): 91-110.

[2] [2] DIXIT R, NASKAR R. Region duplication detection in digital images based on centroid linkage clustering of key-points and graph similarity matching [J]. Multimedia Tools and Applications, 2019, 78(10): 13819-13840.

[3] [3] MENG X Y, DUAN J M, LIU D. Pedestrian detection based on tree-structured graphical model of the human body and hybrid particle swarm clustering [J]. Optics and Precision Engineering, 2018, 26(7): 1802-1812. (in Chinese)

[5] [5] CHEN H W, YUAN X C, WU L S, et al. Automatic point cloud feature-line extraction algorithm based on curvature-mutation analysis [J]. Optics and Precision Engineering, 2019, 27(5): 1218-1228. (in Chinese)

[6] [6] LU S Z, YU H L, WANG X H, et al. Clustering method of raw meal composition based on PCA and kmeans [C]//Proceedings of the 37th Chinese Control Conference. Wuhan, China: IEEE, 2018: 121-135.

[7] [7] YIH J M. FCM algorithm besed on normalized mahalanobis distances in image clustering [C]//Proceedings of 2010 International Conference on Machine Learning and Cybernetics. Qingdao, China: IEEE, 2010: 2724-2729.

[8] [8] ZHANG Z Y, CHENG H M, ZHANG S G. Approach to SOM based correlation clustering [C]//Proceedings of 2008 Chinese Control and Decision Conference. Yantai, China: IEEE, 2008: 121-135.

[9] [9] FREY B J, DUECK D. Clustering by passing messages between data points [J]. Science, 2007, 315(5814): 972-976.

[10] [10] ESTER M, KRIEGEL H P, SANDER J, et al. A density-based algorithm for discovering clusters in large spatial databases with noise [C]//Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining. Portland, Oregon: ACM, 1996: 226-231.

[11] [11] SANDER J, ESTER M, KRIEGEL H P, et al. Density-based clustering in spatial databases: the algorithm GDBSCAN and its applications [J]. Data Mining and Knowledge Discovery, 1998, 2(2): 169-194.

[12] [12] ANKERST M, BREUNIG M M, KRIEGEL H P, et al. OPTICS: ordering points to identify the clustering structure [C]//Proceedings of 1999 ACM SIGMOD International Conference on Management of Data. Philadelphia, Pennsylvania, USA: ACM, 1999: 49-60.

[13] [13] XU X W, JGER J, KRIEGEL H P. A fast parallel clustering algorithm for large spatial databases [J]. Data Mining and Knowledge Discovery, 1999, 3(3): 263-290.

[14] [14] HUANG X H, WANG C, XIONG L Y, et al. A weighting k-means clustering approach by integrating intra-cluster and inter-cluster distances [J]. Chinese Journal of Computers, 2019: 1-15. (in Chinese)

[15] [15] ZHOU S B, XU Z Y, TANG X Q. New method for determining optimal number of clusters in K-means clustering algorithm [J]. Computer Engineering and Applications, 2010, 46(16): 27-31. (in Chinese)

[16] [16] DUDOIT S, FRIDLYAND J. A prediction-based resampling method for estimating the number of clusters in a dataset [J]. Genome Biology, 2002, 3(7): research0036.1.

[17] [17] QIAN P J, ZHOU J X, JIANG Y Z, et al. Multi-view maximum entropy clustering by jointly leveraging inter-view collaborations and intra-view-weighted attributes [J]. IEEE Access, 2018, 6: 28594-28610.

[18] [18] TAO X L, YU L, WANG X Y. One method based on non-negative matrix factorization and fuzzy C means for image clustering [J]. Information Technology and Network Security, 2019, 38(3): 44-48. (in Chinese)

[19] [19] JIA H J, DING S F, MENG L H, et al. A density-adaptive affinity propagation clustering algorithm based on spectral dimension reduction [J]. Neural Computing and Applications, 2014, 25(7/8): 1557-1567.

[20] [20] PAL N R, BEZDEK J C, HATHAWAY R J. Sequential competitive learning and the fuzzy c-means clustering algorithms [J]. Neural Networks, 1996, 9(5): 787-796.

[21] [21] JAIN A K. Data clustering: 50 years beyond K-means [J]. Pattern Recognition Letters, 2010, 31(8): 651-666.