Texture Subspaces

  • Erkki Oja
  • Jussi Parkkinen
Part of the NATO ASI Series book series (volume 30)


The subspace method of pattern recognition has been developed for fast and accurate classification of high-dimensional feature vectors, especially power spectra and distribution densities. The basic algorithms for class subspace construction are statistically motivated, and the classification is based on inner products. In texture analysis, this method has been previously applied for two-dimensional spatial frequency spectra. In this work we show that a feasible method for texture window classification is to use a smoothed cooccurrence matrix as the feature vector and to define texture classes for such representations by the subspace method. These texture subspaces seem to capture the characteristic second-order properties of texture fields. Results are given using various natural and synthetic textures.


Cooccurrence Matrix Subspace Method Subspace Cluster Cooccurrence Matrice Learn Subspace 
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 1987

Authors and Affiliations

  • Erkki Oja
    • 1
  • Jussi Parkkinen
    • 1
  1. 1.Department of Computer Science and MathematicsKuopio UniversityKuopioFinland

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