Neural Computing and Applications

, Volume 21, Issue 8, pp 1893–1904 | Cite as

Local directional derivative pattern for rotation invariant texture classification

  • Zhenhua Guo
  • Qin Li
  • Jane You
  • David Zhang
  • Wenhuang Liu


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.


Texture classification Local binary pattern (LBP) Rotation invariance Local directional derivative pattern (LDDP) 



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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Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  • Zhenhua Guo
    • 1
  • Qin Li
    • 2
  • Jane You
    • 3
  • David Zhang
    • 3
  • Wenhuang Liu
    • 1
  1. 1.Graduate School at ShenzhenTsinghua UniversityShenzhenChina
  2. 2.College of Optoelectronic EngineeringShenzhen UniversityShenzhenChina
  3. 3.Department of ComputingThe Hong Kong Polytechnic UniversityHung HomHong Kong

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