Residual-driven Fuzzy <i>C</i>-Means Clustering for Image Segmentation

Cong Wang; Witold Pedrycz; ZhiWu Li; MengChu Zhou

doi:10.1109/JAS.2020.1003420

Volume 8 Issue 4

Apr. 2021

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 15.3, Top 1 (SCI Q1)

CiteScore: 23.5, Top 2% (Q1)
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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2021 > 8(4): 876-889

Cong Wang, Witold Pedrycz, ZhiWu Li, and MengChu Zhou, "Residual-driven Fuzzy C-Means Clustering for Image Segmentation," IEEE/CAA J. Autom. Sinica, vol. 8, no. 4, pp. 876-889, Apr. 2021. doi: 10.1109/JAS.2020.1003420

Citation:

Cong Wang, Witold Pedrycz, ZhiWu Li, and MengChu Zhou, "Residual-driven Fuzzy C-Means Clustering for Image Segmentation," IEEE/CAA J. Autom. Sinica, vol. 8, no. 4, pp. 876-889, Apr. 2021. doi: 10.1109/JAS.2020.1003420

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Residual-driven Fuzzy C-Means Clustering for Image Segmentation

doi: 10.1109/JAS.2020.1003420

Cong Wang^1
,,
Witold Pedrycz^{2, 3, 4
,},
ZhiWu Li^{1, 5
,
,},
MengChu Zhou^{5, 6
,
,}

1.
School of Electro-Mechanical Engineering, Xidian University, Xi’an 710071, China
2.
Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6R 2V4, Canada
3.
School of Electro-Mechanical Engineering, Xidian University, Xi’an 710071, China
4.
Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
5.
Institute of Systems Engineering, Macau University of Science and Technology, Macau 999078, China
6.
Helen and John C. Hartmann Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102 USA

Funds: This work was supported in part by the Doctoral Students’ Short Term Study Abroad Scholarship Fund of Xidian University, the National Natural Science Foundation of China (61873342, 61672400, 62076189) , the Recruitment Program of Global Experts, and the Science and Technology Development Fund, MSAR (0012/2019/A1)

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Author Bio:
Cong Wang received the B.S. degree in automation and the M.S. degree in mathematics from Hohai University, China, in 2014 and 2017, respectively. He is currently pursuing the Ph.D. degree in mechatronic engineering, Xidian University, China. He was a Visiting Ph.D. Student in the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada. He was a Research Assistant at the School of Computer Science and Engineering, Nanyang Technological University, Singapore. He is currently a Visiting Ph.D. Student in the Department of Electrical and Computer Engineering, National University of Singapore, Singapore. His current research interests include wavelet analysis and its applications, granular computing, as well as image processing

Witold Pedrycz (M’88–SM’90–F’99) received the MS.c. degree in computer science and technology, the Ph.D. degree in computer engineering, and the D.Sci. degree in systems science from the Silesian University of Technology, Gliwice, Poland, in 1977, 1980, and 1984, respectively. He is a Professor and the Canada Research Chair in Computational Intelligence with the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada. He is also with the Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland. He is a foreign member of the Polish Academy of Sciences. He has authored 15 research monographs covering various aspects of computational intelligence, data mining, and software engineering. His current research interests include computational intelligence, fuzzy modeling, and granular computing, knowledge discovery and data mining, fuzzy control, pattern recognition, knowledge-based neural networks, relational computing, and software engineering. He has published numerous papers in the above areas. Dr. Pedrycz was a recipient of the IEEE Canada Computer Engineering Medal, the Cajastur Prize for Soft Computing from the European Centre for Soft Computing, the Killam Prize, and the Fuzzy Pioneer Award from the IEEE Computational Intelligence Society. He is intensively involved in editorial activities. He is an Editor-in-Chief of Information Sciences, an Editor-in-Chief of WIREs Data Mining and Knowledge Discovery (Wiley) and the International Journal of Granular Computing (Springer). He currently serves as a member of a number of editorial boards of other international journals. He is a Fellow of the Royal Society of Canada

ZhiWu Li (M’06–SM’07–F’16) received the B.S. degree in mechanical engineering, the M.S. degree in automatic control, and the Ph.D. degree in manufacturing engineering from Xidian University, Xi’an, China, in 1989, 1992, and 1995, respectively. He joined Xidian University in 1992. He is also currently with the Institute of Systems Engineering, Macau University of Science and Technology, Macau, China. He was a Visiting Professor with the University of Toronto, Toronto, ON, Canada, the Technion-Israel Institute of Technology, Haifa, Israel, the Martin-Luther University of Halle-Wittenburg, Halle, Germany, Conservatoire National des Arts et Métiers, Paris, France, and Meliksah Universitesi, Kayseri, Turkey. His current research interests include Petri net theory and application, supervisory control of discrete-event systems, system reconfiguration, game theory, and data and process mining. Dr. Li was a recipient of an Alexander von Humboldt Research Grant, Alexander von Humboldt Foundation, Germany. He is listed in Marquis Who’s Who in the World, 27th Edition, 2010. He serves as a Frequent Reviewer of 90+ international journals, including Automatica and a number of the IEEE Transactions as well as many international conferences. He is the Founding Chair of Xi’an Chapter of IEEE Systems, Man, and Cybernetics Society. He is a member of Discrete-Event Systems Technical Committee of the IEEE Systems, Man, and Cybernetics Society and IFAC Technical Committee on Discrete-Event and Hybrid Systems, from 2011 to 2014

MengChu Zhou (S’88–M’90–SM’93–F’03) received the B.S. degree in control engineering from Nanjing University of Science and Technology, Nanjing, China in 1983, M.S. degree in automatic control from Beijing Institute of Technology, Beijing, China in 1986, and Ph.D. degree in computer and systems engineering from Rensselaer Polytechnic Institute, Troy, NY, USA in 1990. He joined New Jersey Institute of Technology (NJIT), Newark, NJ, in 1990, and is now a Distinguished Professor of Electrical and Computer Engineering. His research interests are in Petri nets, intelligent automation, Internet of Things, big data, web services, and intelligent transportation. He has over 900 publications including 12 books, 600+ journal papers (450+ in IEEE Transactions, 26 patents and 29 book-chapters. He is the founding Editor of IEEE Press Book Series on Systems Science and Engineering, Editor-in-Chief of IEEE/CAA Journal of Automatica Sinica, and Associate Editor of IEEE Internet of Things Journal, IEEE Transactions on Intelligent Transportation Systems, and IEEE Transactions on Systems, Man, and Cybernetics: Systems. He is a recipient of Humboldt Research Award for US Senior Scientists from Alexander von Humboldt Foundation, Franklin V. Taylor Memorial Award and the Norbert Wiener Award from IEEE Systems, Man and Cybernetics Society, Excellence in Research Prize and Medal from NJIT and Edison Patent Award from the Research & Development Council of New Jersey. He is a life member of Chinese Association for Science and Technology-USA and served as its President in 1999. He is a Fellow of International Federation of Automatic Control (IFAC), American Association for the Advancement of Science (AAAS) and Chinese Association of Automation (CAA)
Corresponding author: ZhiWu Li, e-mail: zhwli@xidian.edu.cn; MengChu Zhou, e-mail: zhou@njit.edu
¹http://www.bic.mni.mcgill.ca/brainweb/
²https: //www2.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/resources.html
³http://research.microsoft.com/vision/cambridge/recognition/
⁴http://neo.sci.gsfc.nasa.gov/
⁵http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html
Received Date: 2020-07-26
Revised Date: 2020-08-27
Accepted Date: 2020-09-24

Available Online: 2020-10-14

Abstract

Abstract

In this paper, we elaborate on residual-driven Fuzzy C-Means (FCM) for image segmentation, which is the first approach that realizes accurate residual (noise/outliers) estimation and enables noise-free image to participate in clustering. We propose a residual-driven FCM framework by integrating into FCM a residual-related regularization term derived from the distribution characteristic of different types of noise. Built on this framework, a weighted $ \ell_{2}$-norm regularization term is presented by weighting mixed noise distribution, thus resulting in a universal residual-driven FCM algorithm in presence of mixed or unknown noise. Besides, with the constraint of spatial information, the residual estimation becomes more reliable than that only considering an observed image itself. Supporting experiments on synthetic, medical, and real-world images are conducted. The results demonstrate the superior effectiveness and efficiency of the proposed algorithm over its peers.
- Fuzzy C-Means,
- image segmentation,
- mixed or unknown noise,
- residual-driven,
- weighted regularization

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¹http://www.bic.mni.mcgill.ca/brainweb/
²https: //www2.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/resources.html
³http://research.microsoft.com/vision/cambridge/recognition/
⁴http://neo.sci.gsfc.nasa.gov/
⁵http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html

References(61)

References

[1]	A. Baraldi and P. Blonda, “A survey of fuzzy clustering algorithms for pattern recognition. I,” IEEE Trans. Syst. Man Cybern. Part B Cybern., vol. 29, no. 6, pp. 778–785, Dec. 1999. doi: 10.1109/3477.809032
[2]	A. Baraldi and P. Blonda, “A survey of fuzzy clustering algorithms for pattern recognition. II,” IEEE Trans. Syst. Man Cybern. Part B Cybern., vol. 29, no. 6, pp. 786–801, Dec. 1999. doi: 10.1109/3477.809033
[3]	C. Subbalakshmi, G. Ramakrishna, and S. K. M. Rao, “Evaluation of data mining strategies using fuzzy clustering in dynamic environment,” in Proc. 3rd Int. Conf. Advanced Computing, Networking and Informatics, New Delhi, 2016, pp. 529−536.
[4]	X. B. Zhu, W. Pedrycz, and Z. W. Li, “Granular encoders and decoders: A study in processing information granules,” IEEE Trans. Fuzzy Syst., vol. 25, no. 5, pp. 1115–1126, Oct. 2017. doi: 10.1109/TFUZZ.2016.2598366
[5]	M. Yambal and H. Gupta, “Image segmentation using fuzzy c means clustering: A survey,” Int. J. Adv. Res. Comput. Commun. Eng., vol. 2, no. 7, pp. 2927–2929, Jul. 2013.
[6]	J. C. Dunn, “A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters,” J. Cybernet., vol. 3, no. 3, pp. 32–57, Sep. 1973. doi: 10.1080/01969727308546046
[7]	J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms. Boston, MA: Springer, 1981.
[8]	J. C. Bezdek, R. Ehrlich, and W. Full, “FCM: The fuzzy C-means clustering algorithm,” Comput. Geosci., vol. 10, no. 2–3, pp. 191–203, Dec. 1984. doi: 10.1016/0098-3004(84)90020-7
[9]	M. N. Ahmed, S. M. Yamany, N. Mohamed, A. A. Farag, and T. Moriarty, “A modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI data,” IEEE Trans. Med. Imag., vol. 21, no. 3, pp. 193–199, Mar. 2002. doi: 10.1109/42.996338
[10]	S. C. Chen and D. Q. Zhang, “Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure,” IEEE Trans. Syst. Man Cybern. Part B Cybern., vol. 34, no. 4, pp. 1907–1916, Aug. 2004. doi: 10.1109/TSMCB.2004.831165
[11]	L. Szilagyi, Z. Benyo, S. M. Szilagyi, and H. S. Adam, “MR brain image segmentation using an enhanced fuzzy C-means algorithm,” in Proc. 25th Annu. Int. Conf. IEEE Engineering in Medicine and Biology Society, Cancun, Mexico, 2003, pp. 724−726.
[12]	W. L. Cai, S. C. Chen, and D. Q. Zhang, “Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation,” Pattern Recognit., vol. 40, no. 3, pp. 825–838, Mar. 2007. doi: 10.1016/j.patcog.2006.07.011
[13]	S. Krinidis and V. Chatzis, “A robust fuzzy local information C-means clustering algorithm,” IEEE Trans. Image Process., vol. 19, no. 5, pp. 1328–1337, May 2010. doi: 10.1109/TIP.2010.2040763
[14]	T. Celik and H. K. Lee, “Comments on “A robust fuzzy local information C-means clustering algorithm,” IEEE Trans. Image Process., vol. 22, no. 3, pp. 1258–1261, Mar. 2013. doi: 10.1109/TIP.2012.2226048
[15]	M. G. Gong, Y. Liang, J. Shi, W. P. Ma, and J. J. Ma, “Fuzzy C-means clustering with local information and kernel metric for image segmentation,” IEEE Trans. Image Process., vol. 22, no. 2, pp. 573–584, Feb. 2013. doi: 10.1109/TIP.2012.2219547
[16]	K. P. Lin, “A novel evolutionary kernel intuitionistic fuzzy C-means clustering algorithm,” IEEE Trans. Fuzzy Syst., vol. 22, no. 5, pp. 1074–1087, Oct. 2014. doi: 10.1109/TFUZZ.2013.2280141
[17]	A. Elazab, C. M. Wang, F. C. Jia, J. H. Wu, G. L. Li, and Q. M. Hu, “Segmentation of brain tissues from magnetic resonance images using adaptively regularized kernel-based fuzzy C-means clustering,” Comput. Math. Methods Med., vol. 2015, Article No. 485495, Dec. 2015.
[18]	F. Zhao, L. C. Jiao, and H. Q. Liu, “Kernel generalized fuzzy C-means clustering with spatial information for image segmentation,” Digit. Signal Process., vol. 23, no. 1, pp. 184–199, Jan. 2013. doi: 10.1016/j.dsp.2012.09.016
[19]	F. F. Guo, X. X. Wang, and J. Shen, “Adaptive fuzzy C-means algorithm based on local noise detecting for image segmentation,” IET Image Process., vol. 10, no. 4, pp. 272–279, Apr. 2016. doi: 10.1049/iet-ipr.2015.0236
[20]	Z. X. Zhao, L. Z. Cheng, and G. Q. Cheng, “Neighbourhood weighted fuzzy C-means clustering algorithm for image segmentation,” IET Image Process., vol. 8, no. 3, pp. 150–161, Mar. 2014. doi: 10.1049/iet-ipr.2011.0128
[21]	X. B. Zhu, W. Pedrycz, and Z. W. Li, “Fuzzy clustering with nonlinearly transformed data,” Appl. Soft Comput., vol. 61, pp. 364–376, Dec. 2017. doi: 10.1016/j.asoc.2017.07.026
[22]	C. Wang, W. Pedrycz, J. B. Yang, M. C. Zhou, and Z. W. Li, “Wavelet frame-based fuzzy C-means clustering for segmenting images on graphs,” IEEE Trans. Cybern., vol. 50, no. 9, pp. 3938–3949, Sep. 2020. doi: 10.1109/TCYB.2019.2921779
[23]	R. R. Gharieb, G. Gendy, A. Abdelfattah, and H. Selim, “Adaptive local data and membership based KL divergence incorporating C-means algorithm for fuzzy image segmentation,” Appl. Soft Comput., vol. 59, pp. 143–152, Oct. 2017. doi: 10.1016/j.asoc.2017.05.055
[24]	C. Wang, W. Pedrycz, Z. W. Li, and M. C. Zhou, Kullback-Leibler divergence-based fuzzy C-means clustering incorporating morphological reconstruction and wavelet frames for image segmentation. 2020. [Online]. Available: arXiv: 2002.09479
[25]	J. Gu, L. C. Jiao, S. Y. Yang, and F. Liu, “Fuzzy double C-means clustering based on sparse self-representation,” IEEE Trans. Fuzzy Syst., vol. 26, no. 2, pp. 612–626, Apr. 2018. doi: 10.1109/TFUZZ.2017.2686804
[26]	C. Wang, W. Pedrycz, M. C. Zhou, and Z. W. Li, “Sparse regularization-based fuzzy C-means clustering incorporating morphological grayscale reconstruction and wavelet frames,” IEEE Trans. Fuzzy Syst., DOI: 10.1109/TFUZZ.2020.2985930.
[27]	L. Vincent, “Morphological grayscale reconstruction in image analysis: Applications and efficient algorithms,” IEEE Trans. Image Process., vol. 2, no. 2, pp. 176–201, Apr. 1993. doi: 10.1109/83.217222
[28]	L. Najman and M. Schmitt, “Geodesic saliency of watershed contours and hierarchical segmentation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 18, no. 12, pp. 1163–1173, Dec. 1996. doi: 10.1109/34.546254
[29]	J. J. Chen, C. R. Su, W. E. L. Grimson, J. L. Liu, and D. H. Shiue, “Object segmentation of database images by dual multiscale morphological reconstructions and retrieval applications,” IEEE Trans. Image Process., vol. 21, no. 2, pp. 828–843, Feb. 2012. doi: 10.1109/TIP.2011.2166558
[30]	T. Lei, X. H. Jia, Y. N. Zhang, L. F. He, H. Y. Meng, and A. K. Nandi, “Significantly fast and robust fuzzy C-means clustering algorithm based on morphological reconstruction and membership filtering,” IEEE Trans. Fuzzy Syst., vol. 26, no. 5, pp. 3027–3041, Oct. 2018. doi: 10.1109/TFUZZ.2018.2796074
[31]	T. Lei, P. Liu, X. H. Jia, X. D. Zhang, H. Y. Meng, and A. K. Nandi, “Automatic fuzzy clustering framework for image segmentation,” IEEE Trans. Fuzzy Syst., vol. 28, no. 9, pp. 2078–2092, Sep. 2020. doi: 10.1109/TFUZZ.2019.2930030
[32]	C. Wang, W. Pedrycz, Z. W. Li, M. C. Zhou, and J. Zhao, “Residual-sparse fuzzy C-means clustering incorporating morphological reconstruction and wavelet frame,” IEEE Trans. Fuzzy Syst., DOI: 10.1109/TFUZZ.2020.3029296.
[33]	X. Z. Bai, Y. X. Zhang, H. N. Liu, and Z. G. Chen, “Similarity measure-based possibilistic FCM with label information for brain MRI segmentation,” IEEE Trans. Cybern., vol. 49, no. 7, pp. 2618–2630, Jul. 2019. doi: 10.1109/TCYB.2018.2830977
[34]	Y. X. Zhang, X. Z. Bai, R. R. Fan, and Z. H. Wang, “Deviation-sparse fuzzy C-means with neighbor information constraint,” IEEE Trans. Fuzzy Syst., vol. 27, no. 1, pp. 185–199, Jan. 2019. doi: 10.1109/TFUZZ.2018.2883033
[35]	A. Fakhry, T. Zeng, and S. W. Ji, “Residual deconvolutional networks for brain electron microscopy image segmentation,” IEEE Trans. Med. Imag., vol. 36, no. 2, pp. 447–456, Feb. 2017. doi: 10.1109/TMI.2016.2613019
[36]	K. Zhang, W. M. Zuo, Y. J. Chen, D. Y. Meng, and L. Zhang, “Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising,” IEEE Trans. Image Process., vol. 26, no. 7, pp. 3142–3155, Jul. 2017. doi: 10.1109/TIP.2017.2662206
[37]	F. Kokkinos and S. Lefkimmiatis, “Iterative residual CNNs for burst photography applications,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, Long Beach, CA, USA, 2019, pp. 5922−5931.
[38]	D. W. Ren, W. M. Zuo, D. Zhang, L. Zhang, and M. H. Yang, “Simultaneous fidelity and regularization learning for image restoration,” IEEE Trans. Pattern Anal. Mach. Intell., DOI: 10.1109/TPAMI.2019.2926357.
[39]	Y. Zhang, X. R. Li, M. Q. Lin, B. Chiu, and M. B. Zhao, “Deep-recursive residual network for image semantic segmentation,” Neural Comput. Appl., vol. 32, no. 16, pp. 12935–12947, Jan. 2020. doi: 10.1007/s00521-020-04738-5
[40]	J. L. Jiang, L. Zhang, and J. Yang, “Mixed noise removal by weighted encoding with sparse nonlocal regularization,” IEEE Trans. Image Process., vol. 23, no. 6, pp. 2651–2662, Jun. 2014. doi: 10.1109/TIP.2014.2317985
[41]	P. Zhou, C. Y. Lu, J. S. Feng, Z. C. Lin and S. C. Yan, “Tensor low-rank representation for data recovery and clustering,” IEEE Trans. Pattern Anal. Mach. Intell., DOI: 10.1109/TPAMI.2019.2954874.
[42]	T. Le, R. Chartrand, and T. J. Asaki, “A variational approach to reconstructing images corrupted by Poisson noise,” J. Math. Imag. Vis., vol. 27, no. 3, pp. 257–263, Apr. 2007. doi: 10.1007/s10851-007-0652-y
[43]	P. J. Huber, “Robust regression: Asymptotics, conjectures and Monte Carlo,” Ann. Stat., vol. 1, no. 5, pp. 799–821, Sep. 1973. doi: 10.1214/aos/1176342503
[44]	P. J. Huber, Robust Statistics. New York: Wiley, 1981.
[45]	C. W. Fang, Z. C. Liao, and Y. Z. Yu, “Piecewise flat embedding for image segmentation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 41, no. 6, pp. 1470–1485, Jun. 2019. doi: 10.1109/TPAMI.2018.2839733
[46]	T. Lei, X. H. Jia, T. L. Liu, S. G. Liu, H. Y. Meng, and A. K. Nandi, “Adaptive morphological reconstruction for seeded image segmentation,” IEEE Trans. Image Process., vol. 28, no. 11, pp. 5510–5523, Nov. 2019. doi: 10.1109/TIP.2019.2920514
[47]	D. N. H. Thanh, D. Sergey, V. B. S. Prasath, and N. H. Hai, “Blood vessels segmentation method for retinal fundus images based on adaptive principal curvature and image derivative operators,” Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., vol. XLII-2/W12, pp. 211–218, May 2019. doi: 10.5194/isprs-archives-XLII-2-W12-211-2019
[48]	D. N. H. Thanh, U. Erkan, V. B. S. Prasath, V. Kumar, and N. N. Hien, “A skin lesion segmentation method for dermoscopic images based on adaptive thresholding with normalization of color models,” in Proc. 6th Int. Conf. Electrical and Electronics Engineering, Istanbul, Turkey, 2019, pp. 116−120.
[49]	A. A. Taha and A. Hanbury, “Metrics for evaluating 3D medical image segmentation: Analysis, selection, and tool,” BMC Med. Imag., vol. 15, Article number: 29, Aug. 2015.
[50]	P. Arbeláez, M. Maire, C. Fowlkes and J. Malik, “Contour detection and hierarchical image segmentation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 5, pp. 898–916, May 2011. doi: 10.1109/TPAMI.2010.161
[51]	C. Wang and J. B. Yang, “Poisson noise removal of images on graphs using tight wavelet frames,” Vis. Comput., vol. 34, no. 10, pp. 1357–1369, Oct. 2018. doi: 10.1007/s00371-017-1418-1
[52]	J. B. Yang and C. Wang, “A wavelet frame approach for removal of mixed Gaussian and impulse noise on surfaces,” Inverse Probl. Imag., vol. 11, no. 5, pp. 783–798, Oct. 2017. doi: 10.3934/ipi.2017037
[53]	C. Wang, Z. Y. Yan, W. Pedrycz, M. C. Zhou, and Z. W. Li, “A weighted fidelity and regularization-based method for mixed or unknown noise removal from images on graphs,” IEEE Trans. Image Process., vol. 29, pp. 5229–5243, Feb. 2020. doi: 10.1109/TIP.2020.2969076
[54]	T. Xu, L. C. Jiao, and W. J. Emery, “SAR image content retrieval based on fuzzy similarity and relevance feedback,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 10, no. 5, pp. 1824–1842, May 2017. doi: 10.1109/JSTARS.2017.2664119
[55]	C. Wang, J. X. Chen, Z. W. Li, E. S. A. Nasr, and A. M. El-Tamimi, “An indicator system for evaluating the development of land-sea coordination systems: A case study of Lianyungang port,” Ecol. Indic., vol. 98, pp. 112–120, Mar. 2019. doi: 10.1016/j.ecolind.2018.10.057
[56]	Z. Cao, X. Xu, B. Hu, M. Zhou, and Q. Li, “Real-time gesture recognition based on feature recalibration network with multi-scale information,” Neurocomputing, vol. 347, pp. 119–130, Jun. 2019. doi: 10.1109/JAS.2017.7510316
[57]	C. Wang, W. Pedrycz, Z. W. Li, M. C. Zhou, and S. S. Ge, “G-image segmentation: Similarity-preserving fuzzy C-means with spatial information constraint in wavelet space,” IEEE Trans. Fuzzy Syst., DOI: 10.1109/TFUZZ.2020.3029285.
[58]	X. Luo, M. C. Zhou, Z. D. Wang, Y. N. Xia, and Q. S. Zhu, “An effective scheme for QoS estimation via alternating direction method-based matrix factorization,” IEEE Trans. Serv. Comput., vol. 12, no. 4, pp. 503–518, Jul.–Aug. 2019. doi: 10.1109/TSC.2016.2597829
[59]	X. Luo, M. C. Zhou, Y. N. Xia, Q. S. Zhu, A. C. Ammari, and A. Alabdulwahab, “Generating highly accurate predictions for missing QoS data via aggregating nonnegative latent factor models,” IEEE Trans. Neural Netw. Learn. Syst., vol. 27, no. 3, pp. 524–537, Mar. 2016. doi: 10.1109/TNNLS.2015.2412037
[60]	X. Luo, H. Wu, H. Q. Yuan, and M. C. Zhou, “Temporal pattern-aware QoS prediction via biased non-negative latent factorization of tensors,” IEEE Trans. Cybern., vol. 50, no. 5, pp. 1798–1809, May 2020. doi: 10.1109/TCYB.2019.2903736
[61]	S. C. Gao, M. C. Zhou, Y. R. Wang, J. J. Cheng, H. Yachi, and J. H. Wang, “Dendritic neuron model with effective learning algorithms for classification, approximation, and prediction,” IEEE Trans. Neural Netw. Learn. Syst., vol. 30, no. 2, pp. 601–614, Feb. 2019. doi: 10.1109/TNNLS.2018.2846646

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For the first time, we propose an RFCM framework for image segmentation.
WRFCM is presented by weighting mixed noise distribution and incorporating spatial information.
We design a two-step iterative algorithm to realize WRFCM.
WRFCM is validated to produce state-of-the-art performance.

Residual-driven Fuzzy C-Means Clustering for Image Segmentation

doi: 10.1109/JAS.2020.1003420

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