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A robust bias-correction fuzzy weighted C-ordered-means clustering algorithm. (English) Zbl 1435.62255
Summary: This paper proposes a modified fuzzy C-means (FCM) algorithm, which combines the local spatial information and the typicality of pixel data in a new fuzzy way. This new algorithm is called bias-correction fuzzy weighted C-ordered-means (BFWCOM) clustering algorithm. It can overcome the shortcomings of the existing FCM algorithm and improve clustering performance. The primary task of BFWCOM is the use of fuzzy local similarity measures (space and grayscale). Meanwhile, this new algorithm adds a typical analysis of data attributes to membership, in order to ensure noise insensitivity and the preservation of image details. Secondly, the local convergence of the proposed algorithm is mathematically proved, providing a theoretical preparation for fuzzy classification. Finally, data classification and real image experiments show the effectiveness of BFWCOM clustering algorithm, having a strong denoising and robust effect on noise images.
MSC:
62H30 Classification and discrimination; cluster analysis (statistical aspects)
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[1] Guizani, S., A k-means clustering-based security framework for mobile data mining, Wireless Communications and Mobile Computing, 16, 18, 3449-3454 (2016)
[2] Wan, L.; Zhang, T.; Xiang, Y.; You, H., A robust fuzzy c-means algorithm based on bayesian nonlocal spatial information for SAR image segmentation, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11, 3, 896-906 (2018)
[3] Gharieb, R.; Gendy, G.; Selim, H., A hard c-means clustering algorithm incorporating membership kl divergence and local data information for noisy image segmentation, International Journal of Pattern Recognition and Artificial Intelligence, 32, 4, 758-769 (2018)
[4] Ouma, Y. O.; Hahn, M., Pothole detection on asphalt pavements from 2D-colour pothole images using fuzzy c-means clustering and morphological reconstruction, Automation in Construction, 83, 196-211 (2017)
[5] Khan, S. S.; Quadri, S. M. K., Structure identification and IO space partitioning in a nonlinear fuzzy system for prediction of patient survival after surgery, International Journal of Intelligent Computing and Cybernetics, 10, 2, 166-182 (2017)
[6] Masulli, F.; Rovetta, S., Soft transition from probabilistic to possibilistic fuzzy clustering, IEEE Transactions on Fuzzy Systems, 14, 4, 516-527 (2006)
[7] Cao, H.; Deng, H.-W.; Wang, Y.-P., Segmentation of M-FISH images for improved classification of chromosomes with an adaptive fuzzy C-means clustering algorithm, IEEE Transactions on Fuzzy Systems, 20, 1, 1-8 (2012)
[8] Javed, A.; Kim, Y.-C.; Khoo, M. C. K.; Ward, S. L. D.; Nayak, K. S., Dynamic 3-D MR visualization and detection of upper airway obstruction during sleep using region-growing segmentation, IEEE Transactions on Biomedical Engineering, 63, 2, 431-437 (2016)
[9] Grau, V.; Mewes, A. U. J.; Alcañiz, M.; Kikinis, R.; Warfield, S. K., Improved watershed transform for medical image segmentation using prior information, IEEE Transactions on Medical Imaging, 23, 4, 447-458 (2004)
[10] Gong, M.; Li, H.; Zhang, X.; Zhao, Q.; Wang, B., Nonparametric statistical active contour based on inclusion degree of fuzzy sets, IEEE Transactions on Fuzzy Systems, 24, 5, 1176-1192 (2016)
[11] Comaniciu, D.; Meer, P., Mean shift: a robust approach toward feature space analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, 5, 603-619 (2002)
[12] Mahapatra, D., Semi-supervised learning and graph cuts for consensus based medical image segmentation, Pattern Recognition, 5, 63, 700-709 (2017)
[13] Li, Z.; Chen, J., Superpixel segmentation using linear spectral clustering, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR ’15)
[14] Chatzis, S. P.; Varvarigou, T. A., A fuzzy clustering approach toward Hidden Markov random field models for enhanced spatially constrained image segmentation, IEEE Transactions on Fuzzy Systems, 16, 5, 1351-1361 (2008)
[15] Pathak, D.; Krahenbuhl, P.; Darrell, T., Constrained convolutional neural networks for weakly supervised segmentation, Proceedings of the 15th IEEE International Conference on Computer Vision (ICCV ’15)
[16] Wang, B.; Tu, Z., Affinity learning via self-diffusion for image segmentation and clustering, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR ’12)
[17] Kim, S.; Yoo, C. D.; Nowozin, S., Image segmentation using higher-order correlation clustering, IEEE Transactions On Pattern Analysis & Machine Intelligence, 36, 9, 1761-1774 (2014)
[18] Pont-Tuset, J.; Arbeląäez, P.; Barron, J. T., Multiscale combinatorial grouping for image segmentation and object proposal generation, IEEE Transactions on Pattern Analysis & Machine Intelligence, 39, 1, 128-140 (2017)
[19] Hasnat, M. A.; Alata, O.; Tremeau, A., Joint color-spatial-directional clustering and region merging (JCSD-RM) for unsupervised RGB-D image segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 38, 11, 2255-2268 (2016)
[20] Bampis, C. G.; Maragos, P.; Bovik, A. C., Graph-driven diffusion and random walk schemes for image segmentation, IEEE Transactions on Image Processing, 26, 1, 35-50 (2017) · Zbl 1409.94024
[21] Saha, P. K.; Basu, S.; Hoffman, E. A., Multiscale opening of conjoined fuzzy objects: Theory and applications, IEEE Transactions on Fuzzy Systems, 24, 5, 1121-1133 (2016)
[22] Dunn, J. C., Some recent investigations of a new fuzzy partitioning algorithm and its application to pattern classification problems, Journal of Cybernetics, 4, 2, 1-15 (1974) · Zbl 0304.68094
[23] Dunn, J. C., Well-separated clusters and optimal fuzzy partitions, Journal of Cybernetics, 4, 1, 95-104 (1974) · Zbl 0304.68093
[24] Bezdek, J. C., Cluster validity with fuzzy sets, Journal of Cybernetics, 3, 3, 58-73 (1974) · Zbl 0294.68035
[25] Bezdek, J. C., Numerical taxonomy with fuzzy sets, Journal of Mathematical Biology, 1, 1, 57-71 (1974) · Zbl 0403.62039
[26] Bezdek, J. C.; Harris, J. D., Convex decompositions of fuzzy partitions, Journal of Mathematical Analysis and Applications, 67, 2, 490-512 (1979) · Zbl 0411.68056
[27] Bezdek, J. C., A convergence theorem for the fuzzy c-means clustering algorithms, IEEE Transactions PAMI, 2, 1, 1-8 (1980) · Zbl 0441.62055
[28] Bezdek, J. C., Pattern Recognition with Fuzzy Objective Function Algorithms (1981), New York, NY, USA: Plenum Press, New York, NY, USA · Zbl 0503.68069
[29] Bezdek, J. C., Hybrid modeling in pattern recognition and control, Knowledge-Based Systems, 8, 6, 359-371 (1995)
[30] Bezdek, J. C.; Ehrlich, R.; Full, W., FCM: the fuzzy c-means clustering algorithm, Computers & Geosciences, 10, 2-3, 191-203 (1984)
[31] Dunn, J. C., A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters department of theoretical and applied mechanics, Journal of Cybernetics, 3, 3, 32-57 (1973) · Zbl 0291.68033
[32] Tolias, Y. A.; Panas, S. M., Image segmentation by a fuzzy clustering algorithm using adaptive spatially constrained membership functions, IEEE Transactions on Systems, Man, and Cybernetics—Part A:Systems and Humans., 28, 3, 359-369 (1998)
[33] Ahmed, M. N.; Yamany, S. M.; Mohamed, N.; Farag, A. A.; Moriarty, T., A modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI data, IEEE Transactions on Medical Imaging, 21, 3, 193-199 (2002)
[34] Pham, D. L., Spatial models for fuzzy clustering, Computer Vision and Image Understanding, 84, 2, 285-297 (2001) · Zbl 1033.68612
[35] Liew, A. W. C.; Leung H, S.; Lau W, H., Fuzzy image clustering incorporating spatial continuity, IEE Proceedings-Vision, Image and Signal Processing, 147, 2, 185-192 (2000)
[36] SzilĺćGyi, L.; Benyĺő, Z.; SzilĺćGyii M, S., MR brain image segmentation using an enhanced fuzzy c-means algorithm, Proceedings of the International Conference of the IEEE Engineering in Medicine & Biology Society
[37] Wu, K.; Yang, M., Alternative c-means clustering algorithms, Pattern Recognition, 35, 10, 2267-2278 (2002) · Zbl 1006.68876
[38] Chen, S.; Zhang, D., Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 34, 4, 1907-1916 (2004)
[39] Yang, M.-S.; Tsai, H.-S., A Gaussian kernel-based fuzzy c-means algorithm with a spatial bias correction, Pattern Recognition Letters, 29, 12, 1713-1725 (2008)
[40] Cai, W.; Chen, S.; Zhang, D., Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation, Pattern Recognition, 40, 3, 825-838 (2007) · Zbl 1118.68133
[41] Wang, J.; Kong, J.; Lu, Y.; Qi, M.; Zhang, B., A modified FCM algorithm for MRI brain image segmentation using both local and non-local spatial constraints, Computerized Medical Imaging and Graphics, 32, 8, 685-698 (2008)
[42] Krinidis, S.; Chatzis, V., A robust fuzzy local information C-means clustering algorithm, IEEE Transactions on Image Processing, 19, 5, 1328-1337 (2010) · Zbl 1371.68306
[43] Gong, M.; Zhou, Z.; Ma, J., Change detection in synthetic aperture radar images based on image fusion and fuzzy clustering, IEEE Transactions on Image Processing, 21, 4, 2141-2151 (2012) · Zbl 1373.94782
[44] Lei, T.; Jia, X.; Zhang, Y.; He, L.; Meng, H.; Nandi, A. K., Significantly fast and robust fuzzy c-means clustering algorithm based on morphological reconstruction and membership filtering, IEEE Transactions on Fuzzy Systems, 26, 5, 3027-3041 (2018)
[45] Saranathan, A. M.; Parente, M., Uniformity-based superpixel segmentation of hyperspectral images, IEEE Transactions on Geoscience and Remote Sensing, 54, 3, 1419-1430 (2016)
[46] Zhao, Z.; Cheng, L.; Cheng, G., Neighbourhood weighted fuzzy c-means clustering algorithm for image segmentation, IET Image Processing, 8, 3, 150-161 (2014)
[47] Guo, F.-F.; Wang, X.-X.; Shen, J., Adaptive fuzzy c-means algorithm based on local noise detecting for image segmentation, IET Image Processing, 10, 4, 272-279 (2016)
[48] Leski, J. M., Fuzzy \(c\)-ordered-means clustering, Fuzzy Sets and Systems, 286, 114-133 (2016) · Zbl 06840609
[49] Huber, P. J., Robust Statistics (1981), New York, NY, USA: John Wiley & Sons, New York, NY, USA · Zbl 0536.62025
[50] Yager, R. R., On ordered weighted averaging aggregation operators in multicriteria decision making, IEEE Transactions on Systems, Man, and Cybernetics, 18, 1, 183-190 (1988) · Zbl 0637.90057
[51] Siminski, K., Fuzzy weighted C-ordered means clustering algorithm, Fuzzy Sets and Systems, 318, 1-33 (2017)
[52] Menaka, E.; Suresh, K. S., Improving segmentation accuracy for detecting deforestation using texture feature derived from landsat 8 oli multispectral imagery, European Journal of Remote Sensing, 48, 1, 169-181 (2015)
[53] Mathworks, Natick M. Image Processing Toolbox, http://www.mathworks.Com
[54] Sanjith, S.; Ganesan, R., Fusion of DWT - DCT algorithm for satellite image compression, International Journal of Applied Engineering Research, 10, 59, 130-137 (2015)
[55] Sanjith, S.; Ganesan, R.; Isaac, R. S., Experimental analysis of compacted satellite image quality using different compression methods, Advanced Science, Engineering and Medicine, 7, 3, 227-233 (2015)
[56] Zhou, F.-B.; Li, C.-G.; Zhu, H.-Q., Research on threshold improved denoising algorithm based on lifting wavelet transform in UV-Vis spectrum, Spectroscopy and Spectral Analysis, 38, 2, 506-510 (2018)
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