Abstract
Spectral clustering has been successfully used in the field of pattern recognition and image processing. The efficiency of spectral clustering, however, depends heavily on the similarity measure adopted. A widely used similarity measure is the Gaussian kernel function where Euclidean distance is used. Unfortunately, the Gaussian kernel function is parameter sensitive and the Euclidean distance is usually not suitable to the complex distribution data. In this paper, a novel similarity measure called kernel fuzzy similarity measure is proposed first, Then this novel measure is integrated into spectral clustering to get a new clustering method: kernel fuzzy similarity based spectral clustering (KFSC). To alleviate the computational complexity of KFSC on image segmentation, Nystr\(\ddot{o}\)m method is used in KFSC. At last, the experiments on three synthetic texture images are made, and the results demonstrate the effectiveness of the proposed algorithm.
Chapter PDF
Similar content being viewed by others
References
Shi, J., Malik, J.: Normalized cuts and image segmentation. In: IEEE Conf. Computer Vision and Pattern Recognition, vol. 3(2), pp. 731–737. IEEE Computer Society (1997)
Yilmaz, A., Javed, O., Shah, M.: Object tracking: A survey. ACM Computing Surveys 38(4), 1–45 (2006)
Chen, S.C., Zhang, D.Q.: 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)
Fowlkes, C., Belongie, S., Chung, F., Malik, J.: Spectral grouping using the Nyström method. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(2), 214–225 (2004)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 888–905 (2000)
Zhao, F., Jiao, L.C., Liu, H., Gao, X.B.: A novel fuzzy clustering algorithm with nonlocal adaptive spatial constraint for image segmentation. Signal Processing 91(4), 988–999 (2011)
Liu, H., Zhao, F., Jiao, L.: Fuzzy spectral clustering with robust spatial information for image segmentation. Applied Soft Computing 12, 3636–3647 (2012)
Liu, H.Q., Jiao, L.C., Zhao, F.: Non-local spatial spectral clustering for image segmentation. Neurocomputing 74(1-3), 461–471 (2011)
Gou, S.P., Zhuang, X., Jiao, L.C.: Quantum Immune Fast Spectral Clustering for SAR Image Segmentation. IEEE Geoscience and Remote Sensing Letters 9(1) (January 2012)
Chen, W., Feng, G.: Spectral clustering with discriminant cuts. Knowledge-Based Systems 28, 27–37 (2012)
Rebagliati, N., Verri, A.: Spectral clustering with more than K eigenvectors. Neurocomputing 74, 1391–1401 (2011)
Su, M.C., Chou, C.H.: A modied version of the K-means algorithm with a distance based on cluster symmetry. IEEE Trans. Pattern Anal. Mach. Intell. 23, 674–680 (2001)
Zelnik-Manor, L., Perona, P.: Self-tuning spectral clustering. In: Advances in Eighteenth Neural Information Processing Systems (NIPS), Vancouver, Canada, pp. 1601–1608 (2004)
Kim, D.W., Lee, K.Y., Lee, D., Lee, K.H.: Evaluation of the performance of clustering algorithms in kernel-induced feature space. Pattern Recognit. 38(4), 607–611 (2005)
Graves, D., Pedrycz, W.: Performance of kernel-based fuzzy clustering. Electron. Lett. 43(25), 1445–1446 (2007)
Graves, D., Pedrycz, W.: Kernel-based fuzzy clustering and fuzzy clustering: A comparative experimental study. Fuzzy Sets Syst. 161(4), 522–543 (2010)
Chen, L., Chen, C.L.P., Lu, M.: A Multiple-Kernel Fuzzy C-Means Algorithm for Image Segmentation. IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics 41(5), 1263–1274 (2011)
Zhang, D.Q., Chen, S.C.: A novel kernelized fuzzy C-means algorithm with application in medical image segmentation. Artif. Intell. Med. 32(1), 37–50 (2004)
Tsai, D.-M., Lin, C.-C.: Fuzzy C-means based clustering for linearly and nonlinearly separable data. Pattern Recognition 44, 1750–1760 (2011)
Zhang, X., Li, J., Yu, H.: Local density adaptive similarity measurement for spectral clustering. Pattern Recognition Letters 32, 352–358 (2011)
Fischer, B., Buhmann, J.M.: Path-based clustering for grouping of smooth curves and texture segmentation. IEEE Trans. Pattern Anal. Machine Intell. 25(4), 513–518 (2003)
Chang, H., Yeung, D.-Y.: Robust path-based spectral clustering. Pattern Recognit. 41(1), 191–203 (2008)
Zhao, F., Liu, H., Jiao, L.: Spectral clustering with fuzzy similarity measure. Digital Signal Processing 21, 701–709 (2011)
Zeyu, L., Shiwei, T., Jing, X., Jun, J.: Modified FCM clustering based on kernel mapping. In: Proc. of the Internat. Society for Optical Engineering, vol. 4554, pp. 241–245 (2001)
Graves, D., Pedrycz, W.: Kernel-based fuzzy clustering and fuzzy clustering: A comparative experimental study. Fuzzy Sets and Systems 161, 522–543 (2010)
Bach, F., Jordan, M.: Learning spectral clustering. In: Proceedings of NIPS 2003, pp. 305–312 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yang, Y., Wang, Y., Cheung, Ym. (2013). Kernel Fuzzy Similarity Measure-Based Spectral Clustering for Image Segmentation. In: Kurosu, M. (eds) Human-Computer Interaction. Towards Intelligent and Implicit Interaction. HCI 2013. Lecture Notes in Computer Science, vol 8008. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39342-6_27
Download citation
DOI: https://doi.org/10.1007/978-3-642-39342-6_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39341-9
Online ISBN: 978-3-642-39342-6
eBook Packages: Computer ScienceComputer Science (R0)