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

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 and 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.

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