Texture Classification by Combining Local Binary Pattern Features and a Self-Organizing Map

  • Markus Turtinen
  • Topi Mäenpää
  • Matti Pietikäinen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2749)


This paper deals with the combined use of Local Binary Pattern (LBP) features and a Self-Organizing Map (SOM) in texture classification. With this approach, the unsupervised learning and visualization capabilities of a SOM are utilized with highly efficient histogram-based texture features. In addition to the Euclidean distance normally used with a SOM, an information theoretic log-likelihood (cumlog) dissimilarity measure is also used for determining distances between feature histograms. The performance of the approach is empirically evaluated with two different data sets: (1) a texture-based visual inspection problem containing four very similar paper classes, and (2) classification of 24 different natural textures from the Outex database.


Local Binary Pattern Texture Classification Dissimilarity Measure 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 2003

Authors and Affiliations

  • Markus Turtinen
    • 1
  • Topi Mäenpää
    • 1
  • Matti Pietikäinen
    • 1
  1. 1.Machine Vision GroupUniversity of OuluFinland

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