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Efficient Image Appearance Description Using Dense Sampling Based Local Binary Patterns

  • Juha Ylioinas
  • Abdenour Hadid
  • Yimo Guo
  • Matti Pietikäinen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7726)

Abstract

This work presents a novel image appearance description method based on the highly popular local binary pattern (LBP) texture features. The key idea consists of introducing a dense sampling encoding strategy for extracting more stable and discriminative texture patterns in local regions. Compared to the conventional sparse sampling scheme commonly used in basic LBP, our proposed dense sampling aims to generate, through a form of up-sampling, more neighboring pixels so that more stable LBP codes, carrying out richer information, are computed. This yields in significantly enhanced image description which is less prone to noise and to sparse and unstable histograms. Another interesting property of the dense sampling scheme is that it can be easily integrated with many existing LBP variants. Extensive experiments on three different classification problems namely face recognition, texture classification and age group estimation on various challenging benchmark databases clearly demonstrate the efficiency of the proposed scheme, showing very promising results compared not only to original LBP but also to state-of-the-art especially in the very demanding task of human age estimation.

Keywords

Face Recognition Local Binary Pattern Dense Sampling Local Binary Pattern Feature Local Binary Pattern Operator 
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 2013

Authors and Affiliations

  • Juha Ylioinas
    • 1
  • Abdenour Hadid
    • 1
  • Yimo Guo
    • 1
  • Matti Pietikäinen
    • 1
  1. 1.Center for Machine Vision ResearchUniversity of OuluFinland

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