Wavelet Domain Features for Texture/Pattern Description, Classification and Replicability Analysis

  • Laurent Balmelli
  • Aleksandra Mojsilović
Part of the Computational Imaging and Vision book series (CIVI, volume 19)


We present a new wavelet domain technique for texture/pattern analysis and test of replicability. The main property of the proposed features is that they measure pattern quality along the most important perceptual dimensions. In other words, we quantify and classify patterns according to their directionality, symmetry, regularity and type of regularity. After the feature extraction, pattern classification (i.e. replicability analysis) is performed by traversing a tree. The algorithm is tested on a database with 340 images demonstrating an excellent classification accuracy. Additionally, we demonstrate the efficiency of our perceptual feature set with an application in texture/pattern retrieval.


Wavelet Coefficient Color Pattern Wavelet Packet Texture Classification Markov Random Field 
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 Science+Business Media Dordrecht 2001

Authors and Affiliations

  • Laurent Balmelli
    • 1
  • Aleksandra Mojsilović
    • 1
  1. 1.IBM Research DivisionT.J. Watson CenterUSA

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