M-band wavelet discrimination of natural textures

Yateen Chitre, Atam P. Dhawan

Research output: Contribution to journalArticlepeer-review

48 Scopus citations

Abstract

The M-band wavelet decomposition, a direct generalization of the standard 2-band wavelet decomposition has been applied to the problem of discriminating natural textures of varying sizes. Regular, M-band filter banks were designed using a genetic algorithm search strategy over the Householder parameter space of M-band wavelets. An exhaustive M-band decomposition was performed on 20 natural textures and energy features were extracted for each decomposed sub-band. The discrimination ability of the extracted features was compared for values of M = 2, 3 and 4. A nearest neighbor algorithm was used to classify a test set of 700 images to an accuracy of 99.5%. The performance was compared with a complete decomposition and decomposition using an irregular M-band filter bank. Statistical tests were used to evaluate the average performance of features extracted from the decomposed sub-bands.

Original languageEnglish (US)
Pages (from-to)773-789
Number of pages17
JournalPattern Recognition
Volume32
Issue number5
DOIs
StatePublished - May 1 1999
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

Keywords

  • Filter bank design
  • Genetic algorithms based search methods
  • K-nearest neighbor classification
  • M-band wavelets
  • Regular wavelets
  • Texture discrimination

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