Learning Multi-scale Representations for Material Classification

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8753)

Abstract

The recent progress in sparse coding and deep learning has made unsupervised feature learning methods a strong competitor to hand-crafted descriptors. In computer vision, success stories of learned features have been predominantly reported for object recognition tasks. In this paper, we investigate if and how feature learning can be used for material recognition. We propose two strategies to incorporate scale information into the learning procedure resulting in a novel multi-scale coding procedure. Our results show that our learned features for material recognition outperform hand-crafted descriptors on the FMD and the KTH-TIPS2 material classification benchmarks.

References

  1. 1.
    Challenges in learning hierarchical models: Transfer learning and optimization. https://sites.google.com/site/nips2011workshop/transfer-learning-challenge
  2. 2.
    Pylearn2 vision, a python library for machine learning. http://deeplearning.net/software/pylearn2/
  3. 3.
    Sparse modeling software, an optimization toolbox for solving various sparse estimation problems. http://spams-devel.gforge.inria.fr/
  4. 4.
    Bengio, Y., Lamblin, P., Popovici, D., Larochelle, H.: Greedy layer-wise training of deep networks. In: NIPS (2007)Google Scholar
  5. 5.
    Bergstra, J., Breuleux, O., Bastien, F., Lamblin, P., Pascanu, R., Desjardins, G., Turian, J., Warde-Farley, D., Bengio, Y.: Theano: a CPU and GPU math expression compiler. In: Proceedings of the Python for Scientific Computing Conference (SciPy) (2010)Google Scholar
  6. 6.
    Caputo, B., Hayman, E., Mallikarjuna, P.: Class-specific material categorisation. In: ICCV (2005)Google Scholar
  7. 7.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 1–27 (2011)CrossRefGoogle Scholar
  8. 8.
    Courville, A., Bergstra, J., Bengio, Y.: A spike and slab restricted boltzmann machine. In: JMLR (2011)Google Scholar
  9. 9.
    Dana, K.J., van Ginneken, B., Nayar, S.K., Koenderink, J.J.: Reflectance and texture of real-world surfaces. ACM Trans. Graph. 18(1), 1–34 (1999)CrossRefGoogle Scholar
  10. 10.
    Farabet, C., Couprie, C., Najman, L., LeCun., Y.: Scene Parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers. In: ICML (2012)Google Scholar
  11. 11.
    Goodfellow, I., Couville, A., Bengio, Y.: Large-scale feature learning with spike-and-slab sparse coding. In: ICML (2012)Google Scholar
  12. 12.
    Hayman, E., Caputo, B., Fritz, M., Eklundh, J.-O.: On the significance of real-world conditions for material classification. In: Pajdla, T., Matas, J.G. (eds.) ECCV 2004. LNCS, vol. 3024, pp. 253–266. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  13. 13.
    Hinton, G., Osindero, S., Teh, Y.: A fast learning algorithm for deep belief nets. Neural Comput. 18, 1527–1554 (2006)MathSciNetCrossRefMATHGoogle Scholar
  14. 14.
    Hu, D., Bo, L., Ren, X.: Toward robust material recognition for everyday objects. In: BMVC (2011)Google Scholar
  15. 15.
    Hussain, S., Triggs, B.: Visual recognition using local quantized patterns. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part II. LNCS, vol. 7573, pp. 716–729. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  16. 16.
    Leung, T., Malik, J.: Representing and recognizing the visual appearance of materials using three-dimensional textons. IJCV 43(1), 29–44 (2001)CrossRefMATHGoogle Scholar
  17. 17.
    Li, W., Fritz, M.: Recognizing materials from virtual examples. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part IV. LNCS, vol. 7575, pp. 345–358. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  18. 18.
    Liu, C., Sharan, L., Adelson, E.H., Rosenholtz, R.: Exploring features in a bayesian framework for material recognition. In: CVPR (2010)Google Scholar
  19. 19.
    Mäenpää, T., Pietikäinen, M.: Multi-scale binary patterns for texture analysis. In: Bigun, J., Gustavsson, T. (eds.) SCIA 2003. LNCS, vol. 2749, pp. 885–892. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  20. 20.
    IEEE Trans. Pattern Anal. Mach. Intell. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. 24(7), 971–987 (2002)Google Scholar
  21. 21.
    Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recogn. 29(1), 51–59 (1996)CrossRefGoogle Scholar
  22. 22.
    Olshausen, B.A., et al.: Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381, 607–609 (1996)CrossRefGoogle Scholar
  23. 23.
    Qi, X., Xiao, R., Guo, J., Zhang, L.: Pairwise rotation invariant co-occurrence local binary pattern. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part VI. LNCS, vol. 7577, pp. 158–171. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  24. 24.
    Raina, R., Battle, A., Lee, H., Packer, B., Ng, A.: Self-taught learning: transfer learning from unlabeled data. In: ICML (2007)Google Scholar
  25. 25.
    Varma, M., Zisserman, A.: A statistical approach to material classification using image patch exemplars. IEEE Trans. Pattern Anal. Mach. Intell. 31(11), 2032–2047 (2009)CrossRefGoogle Scholar
  26. 26.
    Varma, M., Zisserman, A.: Classifying images of materials: achieving viewpoint and illumination independence. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part III. LNCS, vol. 2352, pp. 255–271. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  27. 27.
    Varma, M., Zisserman, A.: A statistical approach to texture classification from single images. Int. J. Comput. Vis. - Special Issue on Texture Anal. Synth. 62(1–2), 61–81 (2005)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Max Planck Institute for InformaticsSaarbrückenGermany

Personalised recommendations