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.
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Keywords
- Face Recognition
- Local Binary Pattern
- Cyclic Shift
- Local Binary Pattern Operator
- Local Binary Pattern Histogram
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Ahonen, T., Matas, J., He, C., Pietikäinen, M. (2009). Rotation Invariant Image Description with Local Binary Pattern Histogram Fourier Features. In: Salberg, AB., Hardeberg, J.Y., Jenssen, R. (eds) Image Analysis. SCIA 2009. Lecture Notes in Computer Science, vol 5575. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02230-2_7
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DOI: https://doi.org/10.1007/978-3-642-02230-2_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02229-6
Online ISBN: 978-3-642-02230-2
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