Volume Local Phase Quantization for Blur-Insensitive Dynamic Texture Classification

  • Juhani Päivärinta
  • Esa Rahtu
  • Janne Heikkilä
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6688)

Abstract

In this paper, we propose a blur-insensitive descriptor for dynamic textures. The Volume Local Phase Quantization (VLPQ) method introduced is based on binary encoding of the phase information of the local Fourier transform at low frequency points and is an extension to the LPQ operator used for spatial texture analysis. The local Fourier transform is computed efficiently using 1-D convolutions for each dimension in a 3-D volume. The data achieved is compressed to a smaller dimension before a scalar quantization procedure. Finally, a histogram of all binary codewords from dynamic texture is formed. The performance of VLPQ was evaluated both in the case of sharp dynamic textures and spatially blurred dynamic textures. Experiments on a dynamic texture database DynTex++ show that the new method tolerates more spatial blurring than LBP-TOP, which is a state-of-the-art descriptor, and its variant LPQ-TOP.

Keywords

Local Phase Quantization Short-Term Fourier Transform spatio-temporal domain blur-insensitivity dynamic texture 

References

  1. 1.
    Doretto, G., Chiuso, A., Wu, Y.N., Soatto, S.: Dynamic Textures. International Journal of Computer Vision 51(2), 91–109 (2003)CrossRefMATHGoogle Scholar
  2. 2.
    Chetverikov, D., Péteri, R.: A Brief Survey of Dynamic Texture Description and Recognition. In: International Conference on Computer Recognition Systems, pp. 17–26 (2005)Google Scholar
  3. 3.
    Péteri, R., Chetverikov, D.: Dynamic Texture Recognition Using Normal Flow and Texture Regularity. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds.) IbPRIA 2005. LNCS, vol. 3523, pp. 223–230. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Zhao, G., Pietikäinen, M.: Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI 2007) 29(6), 915–928 (2007)CrossRefGoogle Scholar
  5. 5.
    Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI 2002) 24(7), 971–987 (2002)CrossRefMATHGoogle Scholar
  6. 6.
    Ghanem, B., Ahuja, N.: Maximum Margin Distance Learning for Dynamic Texture Recognition. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6312, pp. 223–236. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  7. 7.
    Ojansivu, V., Heikkilä, J.: Blur Insensitive Texture Classification Using Local Phase Quantization. In: Elmoataz, A., Lezoray, O., Nouboud, F., Mammass, D. (eds.) ICISP 2008. LNCS, vol. 5099, pp. 236–243. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  8. 8.
    Ojansivu, V., Rahtu, E., Heikkilä, J.: Rotation Invariant Local Phase Quantization for Blur Insensitive Texture Analysis. In: 19th International Conference on Pattern Recognition (ICPR 2008), pp. 1–4. Tampa, FL (2008)CrossRefGoogle Scholar
  9. 9.
    Ahonen, T., Rahtu, E., Ojansivu, V., Heikkilä, J.: Recognition of Blurred Faces Using Local Phase Quantization. In: 19th International Conference on Pattern Recognition (ICPR 2008), pp. 1–4. Tampa, FL (2008)CrossRefGoogle Scholar
  10. 10.
    Péteri, R., Fazekas, S., Huiskes, M.J.: DynTex: A Comprehensive Database of Dynamic Textures. Pattern Recognition Letters 31(12), 1627–1632 (2010), http://www.cwi.nl/projects/dyntex/ CrossRefGoogle Scholar
  11. 11.
    Banham, M.R., Katsaggelos, A.K.: Digital Image Restoration. IEEE Signal Processing Magazine 41(2), 24–41 (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Juhani Päivärinta
    • 1
  • Esa Rahtu
    • 1
  • Janne Heikkilä
    • 1
  1. 1.Machine Vision Group, Department of Electrical and Information EngineeringUniversity of OuluFinland

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