Two New Scale-Adapted Texture Descriptors for Image Segmentation

  • Miguel Angel Lozano
  • Francisco Escolano
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2905)


In texture segmentation it is key to develop descriptors which provide acceptable results without a significant increment of their temporal complexity. In this contribution, we propose two probabilistic texture descriptors: polarity and texture contrast. These descriptors are related to the entropy of both the local distributions of gradient orientation and magnitude. As such descriptors are scale-dependent, we propose a simple method for selecting the optimal scale. Using the features at their optimal scale, we test the performance of these measures with an adaptive version of the ACM clustering method, in which adaptation relies on the Kolmogorov-Smirnov test. Our results with only these two descriptors are very promising.


Image Segmentation Optimal Scale Wavelet Frame Texture Segmentation Gradient Orientation 
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

  • Miguel Angel Lozano
    • 1
  • Francisco Escolano
    • 1
  1. 1.Robot Vision Group, Departamento de Ciencia de la Computación e Inteligencia ArtificialUniversidad de AlicanteSpain

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