Pairwise Rotation Invariant Co-occurrence Local Binary Pattern

  • Xianbiao Qi
  • Rong Xiao
  • Jun Guo
  • Lei Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7577)


In this work, we introduce a novel pairwise rotation invariant co-occurrence local binary pattern (PRI-CoLBP) feature which incorporates two types of context - spatial co-occurrence and orientation co-occurrence. Different from traditional rotation invariant local features, pairwise rotation invariant co-occurrence features preserve relative angle between the orientations of individual features. The relative angle depicts the local curvature information, which is discriminative and rotation invariant. Experimental results on the CUReT, Brodatz, KTH-TIPS texture dataset, Flickr Material dataset, and Oxford 102 Flower dataset further demonstrate the superior performance of the proposed feature on texture classification, material recognition and flower recognition tasks.


Local Binary Pattern Local Binary Pattern Feature Local Binary Pattern Operator Material Recognition Local Binary Pattern Code 
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 2012

Authors and Affiliations

  • Xianbiao Qi
    • 1
  • Rong Xiao
    • 2
  • Jun Guo
    • 1
  • Lei Zhang
    • 2
  1. 1.Beijing University of Posts and TelecommunicationsBeijingP.R. China
  2. 2.Microsoft Research AsiaBeijingP.R. China

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