Unsupervised texture segmentation using feature distributions

  • Timo Ojala
  • Matti Pietikäinen
Session 4: Color & Texture
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1310)


This paper presents an unsupervised texture segmentation method, which uses distributions of local binary patterns and pattern contrasts for measuring the similarity of adjacent image regions during the segmentation process. Nonparametric log-likelihood test, the G statistic, is engaged as a pseudo-metric for comparing feature distributions. A region-based algorithm is developed for coarse image segmentation and a pixelwise classification scheme for improving localization of region boundaries. The performance of the method is evaluated with various types of test images. The same set of parameter values is used in all the experiments with texture mosaics in order to demonstrate the robustness of our approach.


Segmentation Result Local Binary Pattern Natural Scene Texture Region Texture Segmentation 
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 1997

Authors and Affiliations

  • Timo Ojala
    • 1
  • Matti Pietikäinen
    • 1
  1. 1.Machine Vision and Media Processing Group, Infotech OuluUniversity of OuluOuluFinland

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