Abstract
Clustering a given set of data is crucial in many fields including image processing. It plays important roles in image segmentation and object detection for example. This paper proposes a framework of building a similarity matrix for a given dataset, which is then used for clustering the dataset. The similarity between two points are defined based on how other points distribute around the line connecting the two points. It can capture the degree of how the two points are placed on the same line. The similarity matrix is considered as a kernel matrix of the given dataset, and based on it, the spectral clustering is performed. Clustering with the proposed similarity matrix is shown to perform well through experiments using an artificially designed problem and a real-world problem of detecting lines from a distorted image.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice Hall (1988)
Ng, A.Y., Jordan, M.I., Weiss, Y.: On spectral clustering: Analysis and an algorithm. In: NIPS, pp. 849–856 (2001)
Coleman, G.B., Andrews, H.C.: Image segmentation by clustering. Proceedings of the IEEE 67(5), 773–785 (1979)
Avidan, S., Butman, M.: The power of feature clustering: An application to object detection. In: NIPS, pp. 57–64 (2004)
Duda, R.O., Hart, P.E.: Use of the Hough transformation to detect lines and curves in pictures. Commun. ACM 15(1), 11–15 (1972)
Fujiki, J., Akaho, S., Hino, H., Murata, N.: Robust hypersurface fitting based on random sampling approximations. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds.) ICONIP 2012, Part III. LNCS, vol. 7665, pp. 520–527. Springer, Heidelberg (2012)
Suetani, H., Akaho, S.: A RANSAC-based ISOMAP for filiform manifolds in nonlinear dynamical systems – an application to chaos in a dripping faucet. In: Honkela, T. (ed.) ICANN 2011, Part II. LNCS, vol. 6792, pp. 277–284. Springer, Heidelberg (2011)
Bradley, P.S., Mangasarian, O.L.: k-plane clustering. J. of Global Optimization 16(1), 23–32 (2000)
Dhillon, I.S., Guan, Y., Kulis, B.: Kernel k-means: spectral clustering and normalized cuts. In: KDD, pp. 551–556 (2004)
Fujiwara, K., Kano, M., Hasebe, S.: Development of correlation-based clustering method and its application to software sensing. Chemometrics and Intelligent Laboratory Systems 101(2), 130–138 (2010)
Hino, H., Usami, Y., Fujiki, J., Akaho, S., Murata, N.: Calibration of radially symmetric distortion by fitting principal component. In: Jiang, X., Petkov, N. (eds.) CAIP 2009. LNCS, vol. 5702, pp. 149–156. Springer, Heidelberg (2009)
Pelleg, D., Moore, A.W.: X-means: Extending k-means with efficient estimation of the number of clusters. In: ICML, pp. 727–734 (2000)
Kalogeratos, A., Likas, A.: Dip-means: an incremental clustering method for estimating the number of clusters. In: NIPS, pp. 2402–2410 (2012)
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
Hino, H., Fujiki, J., Akaho, S., Mochizuki, Y., Murata, N. (2013). Pairwise Similarity for Line Extraction from Distorted Images. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds) Computer Analysis of Images and Patterns. CAIP 2013. Lecture Notes in Computer Science, vol 8048. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40246-3_31
Download citation
DOI: https://doi.org/10.1007/978-3-642-40246-3_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40245-6
Online ISBN: 978-3-642-40246-3
eBook Packages: Computer ScienceComputer Science (R0)