TEXTURE CLASSIFICATION USING SPARSE K-SVD TEXTON DICTIONARIES

Muhammad Rushdi, Jeffrey Ho

Abstract

This paper addresses the problem of texture classification under unknown viewpoint and illumination variations. We propose an approach that combines sparse K-SVD and texton-based representations. Starting from an analytic or data-driven base dictionary, a sparse dictionary is iteratively estimated from the texture data using the doubly-sparse K-SVD algorithm. Then, for each texture image, K-SVD representations of pixel neighbourhoods are computed and used to assign the pixels to textons. Hence, the texture image is represented by the histogram of its texton map. Finally, a test image is classified by finding the closest texton histogram using the chi-squared distance. Initial experiments on the CUReT database show high classification rates that compare well with Varma-Zisserman MRF results.

References

  1. Cula, O. and Dana, K. (2001). Compact representation of bidirectional texture functions. volume 1, pages I1041 - I-1047 vol.1.
  2. Dana, K., Van-Ginneken, B., Nayar, S., and Koenderink, J. (1999). Reflectance and Texture of Real World Surfaces. ACM Transactions on Graphics (TOG), 18(1):1-34.
  3. Davies, E. (2008). Handbook of Texture Analysis, chapter Introduction to Texture Analysis. Imperial College Press.
  4. Davis, G., Mallat, S., and Avellaneda, M. (1997). Greedy adaptive approximation. J. Constr. Approx., 13:57-98.
  5. Duda, R., Hart, P., and Stork, D. (2001). Pattern Classification. Wiley.
  6. Fritz, M., H. L. C. B. and Eklundh, J.-O. (2004). The kth-tips database. available at www.nada.kth.se/cvap/ databases/kth-tips.
  7. Golub, G. and Loan, C. V. (1989). Matrix Computations. John Hopkins Press.
  8. Hayman, L., C. B. and Eklundh, J.-O. (2004). On the significance of real-world conditions for material classification. In 8th European Conference on Computer Vision, pages 253-266.
  9. Kang, Y., M. K. and Nagahashi, H. (2005). Scale Space and PDE Methods in Computer Vision, volume 3459, chapter Scale Invariant Texture Analysis Using Multiscale Local Autocorrelation Features, pages 363-373. Springer.
  10. Leung, M. and Peterson, A. (1992). Scale and rotation invariant texture classification. In The Twenty-Sixth Asilomar Conference on Signals, Systems and Computers, volume 1, pages 461-465.
  11. Leung, T. and Malik, J. (2001). Representing and recognizing the visual appearance of materials using threedimensional textons. International Journal of Computer Vision, 43.
  12. Liu, G., Lin, Z., and Yu, Y. (2009). Radon representationbased feature descriptor for texture classification. Image Processing, IEEE Transactions on, 18(5):921 - 928.
  13. Loan, C. V. and Pitsianis, N. (1993). Approximation with kronecker products. In Linear Algebra for Large Scale and Real Time Applications, pages 293-314. Kluwer Publications.
  14. Rubinstein, R., Zibulevsky, M., and Elad, M. (2010). Double sparsity: Learning sparse dictionaries for sparse signal approximation. Signal Processing, IEEE Transactions on, 58(3):1553 -1564.
  15. Theodoridis, S. and Koutroumbas, K. (2009). Recognition, 4th Edition. Academic Press.
  16. Varma, M. and Zisserman, A. (2002). Classifying images of materials: Achieving viewpoint and illumination independence.
  17. Varma, M. and Zisserman, A. (2009). A statistical approach to material classification using image patch exemplars. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(11):2032-2047.
  18. Zhao, G. and Pietikainen, M. (2006). Local binary pattern descriptors for dynamic texture recognition. volume 2, pages 211 -214.
Download


Paper Citation


in Harvard Style

Rushdi M. and Ho J. (2011). TEXTURE CLASSIFICATION USING SPARSE K-SVD TEXTON DICTIONARIES . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2011) ISBN 978-989-8425-47-8, pages 187-193. DOI: 10.5220/0003376101870193


in Bibtex Style

@conference{visapp11,
author={Muhammad Rushdi and Jeffrey Ho},
title={TEXTURE CLASSIFICATION USING SPARSE K-SVD TEXTON DICTIONARIES},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2011)},
year={2011},
pages={187-193},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003376101870193},
isbn={978-989-8425-47-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2011)
TI - TEXTURE CLASSIFICATION USING SPARSE K-SVD TEXTON DICTIONARIES
SN - 978-989-8425-47-8
AU - Rushdi M.
AU - Ho J.
PY - 2011
SP - 187
EP - 193
DO - 10.5220/0003376101870193