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Rotation Invariant Image Description with Local Binary Pattern Histogram Fourier Features

  • Timo Ahonen
  • Jiří Matas
  • Chu He
  • Matti Pietikäinen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5575)

Abstract

In this paper, we propose Local Binary Pattern Histogram Fourier features (LBP-HF), a novel rotation invariant image descriptor computed from discrete Fourier transforms of local binary pattern (LBP) histograms. Unlike most other histogram based invariant texture descriptors which normalize rotation locally, the proposed invariants are constructed globally for the whole region to be described. In addition to being rotation invariant, the LBP-HF features retain the highly discriminative nature of LBP histograms. In the experiments, it is shown that these features outperform non-invariant and earlier version of rotation invariant LBP and the MR8 descriptor in texture classification, material categorization and face recognition tests.

Keywords

Face Recognition Local Binary Pattern Cyclic Shift Local Binary Pattern Operator Local Binary Pattern Histogram 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Timo Ahonen
    • 1
  • Jiří Matas
    • 2
  • Chu He
    • 3
    • 1
  • Matti Pietikäinen
    • 1
  1. 1.Machine Vision GroupUniversity of OuluFinland
  2. 2.Center for Machine Percpetion, Dept. of Cybernetics, Faculty of Elec. Eng.Czech Technical University in PragueCzechia
  3. 3.School of Electronic InformationWuhan UniversityP.R. China

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