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Pyramid-Based Multi-structure Local Binary Pattern for Texture Classification

  • Yonggang He
  • Nong Sang
  • Changxin Gao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6494)

Abstract

Recently, the local binary pattern (LBP) has been widely used in texture classification. The conventional LBP methods only describe micro structures of texture images, such as edges, corners, spots and so on, although many of them show a good performance on texture classification. This situation still could not be changed, even though the multiresolution analysis technique is used in methods of local binary pattern. In this paper, we investigate the drawback of conventional LBP operators in describing some textures that has the same small structures but differential large structures. And a multi-structure local binary pattern operator is achieved by executing the LBP method on different layers of image pyramid. The proposed method is simple yet efficient to extract not only the micro structures but also the macro structures of texture images. We demonstrate the performance of our method on the task of rotation invariant texture classification. The experimental results on Outex database show advantages of the proposed method.

Keywords

Local Binary Pattern Texture Image Image Pyramid Macro Structure Local Binary Pattern Histogram 
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 2011

Authors and Affiliations

  • Yonggang He
    • 1
  • Nong Sang
    • 1
  • Changxin Gao
    • 1
  1. 1.Institute for Pattern Recognition and Artificial IntelligenceHuazhong University of Science and TechnologyWuhanChina

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