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Blur Insensitive Texture Classification Using Local Phase Quantization

  • Ville Ojansivu
  • Janne Heikkilä
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5099)

Abstract

In this paper, we propose a new descriptor for texture classification that is robust to image blurring. The descriptor utilizes phase information computed locally in a window for every image position. The phases of the four low-frequency coefficients are decorrelated and uniformly quantized in an eight-dimensional space. A histogram of the resulting code words is created and used as a feature in texture classification. Ideally, the low-frequency phase components are shown to be invariant to centrally symmetric blur. Although this ideal invariance is not completely achieved due to the finite window size, the method is still highly insensitive to blur. Because only phase information is used, the method is also invariant to uniform illumination changes. According to our experiments, the classification accuracy of blurred texture images is much higher with the new method than with the well-known LBP or Gabor filter bank methods. Interestingly, it is also slightly better for textures that are not blurred.

Keywords

Discrete Fourier Transform Test Suite Point Spread Function Local Binary Pattern Texture Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Tuceryan, M., Jain, A.K.: Texture analysis. In: Chen, C.H., Pau, L.F., Wang, P.S.P. (eds.) The Handbook of Pattern Recognition and Computer Vision, pp. 207–248. World Scientific Publishing Co, Singapore (1998)Google Scholar
  2. 2.
    van de Weijer, J., Schmid, C.: Blur robust and color constant image decription. In: Proc. IEEE International Conference on Image Processing (ICIP 2006), Atlanta, Georgia, October 2006, pp. 993–996 (2006)Google Scholar
  3. 3.
    Flusser, J., Suk, T.: Degraded image analysis: An invariant approach. IEEE Trans. Pattern Anal. Machine Intell. 20(6), 590–603 (1998)CrossRefGoogle Scholar
  4. 4.
    Ojansivu, V., Heikkilä, J.: A method for blur and similarity transform invariant object recognition. In: Proc. International Conference on Image Analysis and Processing (ICIAP 2007), Modena, Italy, September 2007, pp. 583–588 (2007)Google Scholar
  5. 5.
    Banham, M.R., Katsaggelos, A.K.: Digital image restoration. IEEE Signal Processing Mag. 14(2), 24–41 (1997)CrossRefGoogle Scholar
  6. 6.
    Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Machine Intell. 24(7), 971–987 (2002)CrossRefGoogle Scholar
  7. 7.
    Randen, T., Husøy, J.H.: Filtering for texture classification: A comparative study. IEEE Trans. Pattern Anal. Machine Intell. 21(4), 291–310 (1999)CrossRefGoogle Scholar
  8. 8.
    Manjunathi, B.S., Ma, W.Y.: Texture features for browsing and retrieval of image data. IEEE Trans. Pattern Anal. Machine Intell. 18(8), 837–842 (1996)CrossRefGoogle Scholar
  9. 9.
    Vo, A.P., Oraintara, S., Nguyen, T.T.: Using phase and magnitude information of the complex directional filter bank for texture image retrieval. In: Proc. IEEE International Conference on Image Processing (ICIP 2007), San Antonio, Texas, September 2007, pp. 61–64 (2007)Google Scholar
  10. 10.
    Xiuwen, L., DeLiang, W.: Texture classification using spectral histograms. IEEE Trans. Image Processing 12(6), 661–670 (2003)CrossRefGoogle Scholar
  11. 11.
    Ojala, T., Mäenpää, T., Pietikäinen, M., Viertola, J., Kyllönen, J., Huovinen, S.: Outex - new framework for empirical evaluation of texture analysis algorithms. In: Proc. 16th International Conference on Pattern Recognition (ICPR 2002), August 2002, pp. 701–706 (2002)Google Scholar
  12. 12.
    Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distribution. Pattern Recognition 29(1), 51–59 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Ville Ojansivu
    • 1
  • Janne Heikkilä
    • 1
  1. 1.Machine Vision Group, Department of Electrical and Information EngineeringUniversity of OuluPO Box 4500Finland

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