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.
|Pages (from-to)||180 - 188|
|Journal||IEE Proceedings - Vision, Image and Signal Processing|
|Publication status||Published - Jun 1997|
Bibliographical notePublisher: Institution of Electrical Engineers (IEE)
- Gabor filter
- rotation invariance
- texture classification