Abstract
The kernel weighted fuzzy c-means clustering with local information (KWFLICM) algorithm performs robustly to noise in research related to image segmentation using fuzzy c-means (FCM) clustering algorithms, which incorporate image local neighborhood information. However, KWFLICM performs poorly on images contaminated with a high degree of noise. This work presents a kernel possibilistic fuzzy c-means with a local information (KWPFLICM) algorithm to overcome the noise-related deficiencies of KWFLICM. The proposed approach leverages the robustness to noise of the kernel possibilistic fuzzy c-means (KPFCM) algorithm, which is a hybridization of the kernel possibilistic c-means (KPCM) and kernel FCM (KFCM), rather than relying on the kernel FCM algorithm. Experiments performed on the various types of images degraded by different degrees of noises prove that proposed algorithm is effectual and efficient, and more robust to noise.
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References
Pal, N.R., Pal, S.K.: A review on image segmentation techniques. Pattern Recognit. 26(9), 1277–1294 (1993)
Naz, S., Majeed, H., Irshad, H.: Image segmentation using fuzzy clustering: a survey. In: 2010 6th International Conference on Emerging Technologies (ICET), pp. 181–186. IEEE (2010)
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 Trans. Med. Imaging 21(3), 193–199 (2002)
Chen, S., Zhang, D.: Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 34(4), 1907–1916 (2004)
Szilagyi, L., Benyo, Z., Szilágyi, S.M., Adam, H.S.: MR brain image segmentation using an enhanced fuzzy c-means algorithm. In: Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2003, vol. 1, pp. 724–726. IEEE (2003)
Cai, W., Chen, S., Zhang, D.: Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation. Pattern Recognit. 40(3), 825–838 (2007)
Krinidis, S., Chatzis, V.: A robust fuzzy local information c-means clustering algorithm. IEEE Trans. Image Process. 19(5), 1328–1337 (2010)
Gong, M., Zhou, Z., Ma, J.: Change detection in synthetic aperture radar images based on image fusion and fuzzy clustering. IEEE Trans. Image Process. 21(4), 2141–2151 (2012)
Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press, Cambridge (2000)
Zhang, D.-Q., Chen, S.-C., Pan, Z.-S., Tan, K.-R.: Kernel-based fuzzy clustering incorporating spatial constraints for image segmentation. In: 2003 International Conference on Machine Learning and Cybernetics, vol. 4, pp. 2189–2192. IEEE (2003)
Wu, X.-H., Zhou, J.-J.: Possibilistic fuzzy c-means clustering model using kernel methods. In: 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, International Conference on Computational Intelligence for Modelling, Control and Automation, vol. 2, pp. 465–470. IEEE (2005)
Wu, K.-L., Yang, M.-S.: Alternative c-means clustering algorithms. Pattern Recognit. 35(10), 2267–2278 (2002)
Chen, L., Chen, C.L.P., Lu, M.: A multiple-kernel fuzzy c-means algorithm for image segmentation. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 41(5), 1263–1274 (2011)
Gong, M., Liang, Y., Shi, J., Ma, W., Ma, J.: Fuzzy c-means clustering with local information and kernel metric for image segmentation. IEEE Trans. Image Process. 22(2), 573–584 (2013)
Memon, K.H., Lee, D.-H.: Generalised kernel weighted fuzzy c-means clustering algorithm with local information. Fuzzy Sets Syst. 340, 91–108 (2018)
Memon, K.H., Lee, D.-H.: Generalised fuzzy c-means clustering algorithm with local information. IET Image Process. 11(1), 1–12 (2016)
Pal, N.R., Pal, K., Keller, J.M., Bezdek, J.C.: A possibilistic fuzzy c-means clustering algorithm. IEEE Trans. Fuzzy Syst. 13(4), 517–530 (2005)
Zhang, H., Fritts, J.E., Goldman, S.A.: An entropy-based objective evaluation method for image segmentation. In: Storage and Retrieval Methods and Applications for Multimedia 2004, vol. 5307, pp. 38–50. International Society for Optics and Photonics (2003)
Krishnapuram, R., Kim, J.: Clustering algorithms based on volume criteria. IEEE Trans. Fuzzy Syst. 8(2), 228–236 (2000)
Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings of Eighth IEEE International Conference on Computer Vision, 2001. ICCV 2001, vol. 2, pp. 416–423. IEEE (2001)
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Memon, K.H., Memon, S., Qureshi, M.A. et al. Kernel Possibilistic Fuzzy c-Means Clustering with Local Information for Image Segmentation. Int. J. Fuzzy Syst. 21, 321–332 (2019). https://doi.org/10.1007/s40815-018-0537-9
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DOI: https://doi.org/10.1007/s40815-018-0537-9