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Local directional derivative pattern for rotation invariant texture classification

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Abstract

Local binary pattern (LBP) is a simple and efficient operator to describe local image pattern. It could be regarded as a binary representation of 1st order derivative between the central and its neighbors. Based on LBP definition, in this paper, a framework of local directional derivative pattern (LDDP) is proposed which could represent high order directional derivative feature, and LBP is a special case of LDDP. Under the proposed framework, like traditional LBP, rotation invariance could be easily defined. As different order derivative information contains complementary features, better recognition accuracy could be achieved by combining different order LDDPs which is validated by two large public texture databases, Outex and CUReT.

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Acknowledgments

The authors wish to thank the anonymous reviewers for the constructive advice on the revision of the manuscript. The authors sincerely thank MVG and VGG for sharing the source codes of LBP and VZ_MR8. The funding support from Hong Kong Government under its GRF scheme (5341/08E and 5366/09E), the research grant from the Hong Kong Polytechnic University (1-ZV5U), and the NSFC (Nos. 60803090 and 61020106004) are greatly appreciated.

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Authors and Affiliations

  1. Graduate School at Shenzhen, Tsinghua University, Shenzhen, China

    Zhenhua Guo & Wenhuang Liu

  2. College of Optoelectronic Engineering, Shenzhen University, Shenzhen, China

    Qin Li

  3. Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong

    Jane You & David Zhang

Authors
  1. Zhenhua Guo
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  2. Qin Li
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  3. Jane You
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  4. David Zhang
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  5. Wenhuang Liu
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Correspondence to Jane You.

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Guo, Z., Li, Q., You, J. et al. Local directional derivative pattern for rotation invariant texture classification. Neural Comput & Applic 21, 1893–1904 (2012). https://doi.org/10.1007/s00521-011-0586-6

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  • DOI: https://doi.org/10.1007/s00521-011-0586-6

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