Abstract
Spectral clustering algorithm has been a research hotspot in the field of image processing, recent years. Spectral clustering based on the similarity of data while structure of similarity matrix is complex. The calculation of spectral clustering can be very time-consuming, especially in the process of Eigen-decomposition for Laplacian matrix. Nyström extension method could obtain the approximation solution of eigenvectors by using a small amount of sample information, reduce the computational complexity of spectral clustering effectively. Based on the features of image and the error analysis of Nyström a new sampling method is presented. Using Uniform Sampling generates a set of cluster centers at first; then, minimize the error between data and centers by iteration; finally, typical experiment results and analysis are given.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Zhang, Y.: A survey on transition region-based techniques for image segmentation. J. Comput. Aided Des. Comput. Graph. 27(3), 379–381 (2015)
Zhu, Z., Wang, L.: Initialization approach for fuzzy C-means algorithm for color image segmentation. Appl. Res. Comput. 32(4), 1257–1260 (2015)
Shi, J., Maiik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888–905 (2002)
Belkin, M.: Laplacian eigen maps and spectral techniques for embedding and clustering. In: Dietterich, T.G., Becker, S., Ghahramani, Z. (eds.) Advances in Neural Information Processing Systems, vol. 14, pp. 585–591. MIT Press, Cambridge (2002)
Ng, A.Y., Jordan, M.I., Weiss, Y.: On spectral clustering: analysis and an algorithm. In: Advances in Neural Information Processing Systems. MIT Press, Cambridge (2002)
Lu, Z., Carreira-Perpinan, M.A.: Constrained spectral clustering through affinity propagation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8(2008)
Fowlkes, C., Belongie, S., Chung, F., Malik, J.: Spectral grouping using Nyström extension. IEEE Trans. Pattern Anal. Mach. Intell. 26(2), 214–225 (2004)
Zhang, K., Tsang, I.W., Kwok, J.T.: Improved Nyström low-rank approximation and error analysis. In: Proceedings of the 25th International Conference on Machine learning, Helsinki, pp. 1232–1239 (2008)
Zhang, K., Kwok, J.T.: Clustered Nyström method for large scale manifold learning and dimension reduction. JEEE Trans. Neural Netw. 21(10), 1576–1587 (2010)
Wang, S., Gu, J., Chen, F.: Clustering high-dimensional data via spectral clustering using collaborative representation coefficients. In: Huang, D.-S., Jo, K.-H., Hussain, A. (eds.) ICIC 2015. LNCS, vol. 9226, pp. 248–258. Springer, Heidelberg (2015)
Chen, Z., Qiu, Z., Li, J., et al.: Two-derivative Runge-Kutta-Nyström methods for second-order ordinary differential equations. Numer. Algorithms 70(4), 897–927 (2015)
Acknowledgement
Project supported by the Natural Science Foundation of China (61362034).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Zhongmin, L., Bohao, L., Zhanming, L., Wenjin, H. (2016). Error Based Nyström Spectral Clustering Image Segmentation. In: Huang, DS., Jo, KH. (eds) Intelligent Computing Theories and Application. ICIC 2016. Lecture Notes in Computer Science(), vol 9772. Springer, Cham. https://doi.org/10.1007/978-3-319-42294-7_49
Download citation
DOI: https://doi.org/10.1007/978-3-319-42294-7_49
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-42293-0
Online ISBN: 978-3-319-42294-7
eBook Packages: Computer ScienceComputer Science (R0)