Visual Recognition Using Local Quantized Patterns

  • Sibt ul Hussain
  • Bill Triggs
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7573)


Features such as Local Binary Patterns (LBP) and Local Ternary Patterns (LTP) have been very successful in a number of areas including texture analysis, face recognition and object detection. They are based on the idea that small patterns of qualitative local gray-level differences contain a great deal of information about higher-level image content. Current local pattern features use hand-specified codings that are limited to small spatial supports and coarse graylevel comparisons. We introduce Local Quantized Patterns (LQP), a generalization that uses lookup-table-based vector quantization to code larger or deeper patterns. LQP inherits some of the flexibility and power of visual word representations without sacrificing the run-time speed and simplicity of local pattern ones. We show that it outperforms well-established features including HOG, LBP and LTP and their combinations on a range of challenging object detection and texture classification problems.


Face Recognition Object Detection Local Binary Pattern Local Pattern Average Precision 
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

  • Sibt ul Hussain
    • 1
  • Bill Triggs
    • 2
  1. 1.GREYC, CNRS UMR 6072, Université de CaenFrance
  2. 2.Laboratoire Jean KuntzmannGrenobleFrance

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