Abstract
In recent years, clustering techniques have become a useful tool in exploring data structures and have been employed in a broad range of applications. In this paper we derive a novel image clustering approach based on a sparse representation model, which assumes that each instance can be reconstructed by the sparse linear combination of other instances. Our method characterizes the graph adjacency structure and graph weights by sparse linear coefficients computed by solving ℓ1-minimization. Spectral clustering algorithm using these coefficients as graph weight matrix is then used to discover the cluster structure. Experiments confirmed the effectiveness of our approach.
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References
Ng, A., Jordan, M., Weiss, Y.: On spectral clustering: analysis and an algorithm. In: Advances in Neural Information Processing Systems, vol. 14, pp. 849–856 (2002)
Cai, D., He, X., Han, J.: Document clustering using locality preserving indexing. IEEE Transactions on Knowledge and Data Engineering 17, 1624–1637 (2005)
Amaldi, E., Kann, V.: On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems. Theoretical Computer Science 209, 237–260 (1998)
Xing, E.P., Ng, A.Y., Jordan, M.I., Russell, S.: Distance metric learning, with application to clustering with side-information. In: Proceedings of Neural Information Processing Systems, vol. 15, pp. 505–512 (2002)
Bach, F.R., Jordan, M.I.: Learning Spectral Clustering, With Application To Speech Separation. Journal of Machine Learning Research 7, 1963–2001 (2006)
Yu, H., Li, M.J., Zhang, H.J., Feng, J.F.: Color texture moments for content-based image retrieval. In: Proc. International Conference on Image Processing, vol. 3, pp. 929–932 (2002)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 888–905 (2000)
Malik, J., Ganesh, A., Yang, A., Ma, Y.: Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 31, 210–227 (2008)
Wagstaff, K., Cardie, C., Rogers, S., Schroedl, S.: Constrained K-means clustering with background knowledge. In: Proc. the Eighteenth International Conference on Machine Learning, pp. 577–584 (2001)
Kamvar, S.D., Klein, D., Manning, C.D.: Spectral learning. In: Proc. the Eighteenth International Joint Conference on Artificial Intelligence, pp. 561–566 (2003)
Yan, S.C., Wang, H.: Semi-supervised Learning by Sparse Representation. In: Proc. SIAM Data Mining Conference, pp. 792–801 (2009)
Yu, S.X., Shi, J.: Grouping with bias. In: Advances in Neural Information Processing Systems (2002)
Weiss, Y.: Segmentation using eigenvectors: a unifying view. In: Proc. of the Seventh International Conference on Computer Vision, vol. 2, p. 975 (1999)
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Jiao, J., Mo, X., Shen, C. (2010). Image Clustering via Sparse Representation. In: Boll, S., Tian, Q., Zhang, L., Zhang, Z., Chen, YP.P. (eds) Advances in Multimedia Modeling. MMM 2010. Lecture Notes in Computer Science, vol 5916. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11301-7_82
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DOI: https://doi.org/10.1007/978-3-642-11301-7_82
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-11300-0
Online ISBN: 978-3-642-11301-7
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