Bezdek J C. Pattern recognition with fuzzy objective function algorithms [M]. New York: Plenum Press, 1981.
[2]
Pham D L, Prince J L. Adaptive fuzzy segmentation of magnetic resonance images [J]. IEEE Transactions on Medical Imaging, 1999, 18 (9): 737-752.
[3]
Liew A W C, Yan H, Law N F. Image segmentation based on adaptive cluster prototype estimation [J]. IEEE Transactions on Fuzzy Systems, 2005, 13 (4): 444-453.
[4]
Cai W, Chen S, Zhang D Q. Fast and robust fuzzy c-means algorithms incorporating local information for image segmentation [J]. Pattern Recognition, 2007, 40(3): 825-838.
[5]
Chen S C, Zhang D Q. Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure [J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2004, 34(4): 1907-1916.
[6]
Krinidis S, Chatzis V. A robust fuzzy local information C-Means clustering algorithm [J]. IEEE Transactions on Image Process, 2010, 19 (6): 1328-1337.
[7]
Guptal L, Sortrakul T. A Gaussian mixture based image segmentation algorithm [J]. Pattern Recognition, 1998, 31(3): 315-325.
[8]
Diplaros A, Vlassis N, Gevers T. A spatially constrained generative model and an EM algorithm for image segmentation [J]. IEEE Transactions on Neural Networks, 2007, 18(3): 798-808.
[9]
Nikou C, Glatsanos N, Likas A. A class-adaptive spatially variant mixture model for image segmentation [J]. IEEE Transactions on Image Processing, 2007, 16(4): 1121-1130.
[10]
Nguyen T, Wu Q. Fast and robust spatially constrained gaussian mixture model for image segmentation [J]. IEEE Transactions on Circuits and Systems for Video Technology, 2013, 23(4): 621-635.
[11]
Tran D, Le T, Wagner M. Fuzzy Gaussian mixture models for speaker recognition [C] // Proceedings of the International Conference on Spoken Language. Australia: University of Western Australia, 1998, 759-762.
[12]
Zeng J, Xie L, Liu Z. Type-2 fuzzy Gaussian mixture [J]. Pattern Recognition, 2008, 41(12): 3636-3643.
[13]
Chatzis S, Varvarigou T A. A Fuzzy clustering approach toward hidden markov random field models for enhanced spatially constrained image segmentation [J]. IEEE Transactions on Fuzzy Systems, 2008, 16(5): 1351-1361.
[14]
Zhang H, Wu J Q M, Nguyen T M. A robust fuzzy algorithm based on student\'s t-distribution and mean template for image segmentation application [J]. IEEE Signal Processing Letters, 2013, 20(2): 117-120.
[15]
Ichihashi H, Miyagishi K, Honda K. Fuzzy c-means clustering with regularization by K-L information [C] // Proceedings of 10th IEEE Int. Conf. Fuzzy Syst. Piscataway, NJ, USA: IEEE, 2001, 924-927.
[16]
Kokkinos I, Eyangelopoulos G, Maragos P. Texture analysis and segmentation using modulation features, generative models and weighted curve evolution [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(1): 142-157.
[17]
Martin D T D, Fowlkes C, Tal D, et al. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics [C] // Proceedings of the Eighth International Conference on Computer Vision. Vancouver, BC, United states: Institute of Electrical and Electronics Engineers Inc, 2001: 416-423.
[18]
Comanicu D, Meer P. Mean shift: a robust approach toward feature space analysis [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(5): 603-619.
[19]
Shi J, Malik J. Normalized cuts and image segmentation [C] // Proceedings of International Conference on Computer Vision and Pattern Recognition. San Juan: IEEE, 1997: 731-737.
[20]
Yang A Y, Wright J, Ma Y, et al. Unsupervised segmentation of natural images via lossy data compression [J]. Computer Vision and Image Understanding, 2008, 110(2): 212-225.
[21]
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[22]
Chen S F, Cao L, Wang Y, et al. Image segmentation by MAP-ML estimations [J]. IEEE Transactions on Image Processing, 2010, 19(9): 2254-2264.
