Abstract
Clustering has been among the most active research topics in machine learning and pattern recognition. Though recent approaches have delivered impressive results in a number of challenging clustering tasks, most of them did not solve two problems. First, most approaches need prior knowledge about the number of clusters which is not practical in many applications. Second, non-linear and elongated clusters cannot be clustered correctly. In this paper, a general framework is proposed to solve both problems by convex clustering based on learned distance. In the proposed framework, the data is transformed from elongated structures into compact ones by a novel distance learning algorithm. Then, a convex clustering algorithm is used to cluster the transformed data. Presented experimental results demonstrate successful solutions to both problems. In particular, the proposed approach is very suitable for superpixel generation, which are a common base for recent high level image segmentation algorithms.
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.
Similar content being viewed by others
References
Han, J., Kamber, M.: Data mining: Concepts and techniques. Morgan Kaufmann, San Francisco (2000)
Ng, A.Y., Jordan, M.I., Weiss, Y.: On spectral clustering: Analysis and an algorithm. In: Advances in Neural Information Processing Systems, vol. 14, pp. 849–856 (2002)
Fischer, B., Buhmann, J.M.: Path-based clustering for grouping of smooth curves and texture segmentation. IEEE. PAMI 25, 513–518 (2003)
Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. International Journal of Computer Vision 59, 167–181 (2004)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE. PAMI (2000)
Moore, A., Prince, S., Warrell, J., Mohammed, U., Jones, G.: Superpixel lattices. In: CVPR (2008)
Tenenbaum, J.B., de Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science, 290 (2000)
Roweis, S., Saul, L.: Nonlinear dimensionality reduction by locally linear embedding. Science (2000)
Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation 15 (2003)
Gonzales, R., Woods, R.: Digital image processing. Addison-Wesley, Reading (1992)
Cox, T., Cox, M.: Multidimensional scaling. Chapman and Hall, Boca Raton (1994)
Frey, B.J., Dueck, D.: Clustering by passing messages between data points. Science 315, 972–976 (2007)
Lashkari, D., Golland, P.: Convex clustering with exemplar-based models. In: Advances in Neural Information Processing Systems (2007)
Borenstein, E., Shron, E., Ullman, S.: Combining top-down and bottom-up segmentation. In: Proc. IEEE workshop on Perc. Org. in Com. Vis. (2004)
Fischer, B., Roth, V., Buhmann, J.M.: Clustering with the connectivity kernel. In: Advances in Neural Information Processing Systems (2004)
Chen, Q., Sun, Q., Heng, P.A., Xia, D.: A double-threshold image binarization method based on edge detector. Pattern reconition 41, 1254–1267 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yang, X., Latecki, L.J., Gross, A. (2009). Distance Learning Based on Convex Clustering. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2009. Lecture Notes in Computer Science, vol 5876. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10520-3_71
Download citation
DOI: https://doi.org/10.1007/978-3-642-10520-3_71
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10519-7
Online ISBN: 978-3-642-10520-3
eBook Packages: Computer ScienceComputer Science (R0)