Robust rotation-invariant texture classification: wavelet, Gabor filter and GMRF based schemes

R Porter, CN Canagarajah

Research output: Contribution to journalArticle (Academic Journal)peer-review

172 Citations (Scopus)

Abstract

Three novel feature extraction schemes for texture classification are proposed. The schemes employ the wavelet transform, a circularly symmetric Gabor filter or a Gaussian Markov random field with a circular neighbour set to achieve rotation-invariant texture classification. The schemes are shown to give a high level of classification accuracy compared to most existing schemes, using both fewer features (four) and a smaller area of analysis (16×16). Furthermore, unlike most existing schemes, the proposed schemes are shown to be rotation invariant demonstrate a high level of robustness to noise. The performances of the three schemes are compared, indicating that the wavelet-based approach is the most accurate, exhibits the best noise performance and has the lowest computational complexity.
Original languageEnglish
Pages (from-to)180 - 188
JournalIEE Proceedings - Vision, Image and Signal Processing
Volume144
Issue number3
DOIs
Publication statusPublished - Jun 1997

Bibliographical note

Publisher: Institution of Electrical Engineers (IEE)

Keywords

  • GMRF
  • Gabor filter
  • rotation invariance
  • wavelets
  • texture classification

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