Adaptive Matrices for Color Texture Classification

  • Kerstin Bunte
  • Ioannis Giotis
  • Nicolai Petkov
  • Michael Biehl
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6855)


In this paper we introduce an integrative approach towards color texture classification learned by a supervised framework. Our approach is based on the Generalized Learning Vector Quantization (GLVQ), extended by an adaptive distance measure which is defined in the Fourier domain and 2D Gabor filters. We evaluate the proposed technique on a set of color texture images and compare results with those achieved by methods already existing in the literature. The features learned by GLVQ improve classification accuracy and they generalize much better for evaluation data previously unknown to the system.


adaptive metric Gabor filter color texture analysis classification Learning Vector Quantization 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Kerstin Bunte
    • 1
  • Ioannis Giotis
    • 1
  • Nicolai Petkov
    • 1
  • Michael Biehl
    • 1
  1. 1.Johann Bernoulli Institute for Mathematics and Computer ScienceUniversity of GroningenThe Netherlands

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