Issue No. 05 - May (2003 vol. 25)
<p><b>Abstract</b>—Classification of texture images, especially those with different orientation and scale changes, is a challenging and important problem in image analysis and classification. This paper proposes an effective scheme for rotation and scale invariant texture classification using log-polar wavelet signatures. The rotation and scale invariant feature extraction for a given image involves applying a <it>log-polar transform</it> to eliminate the rotation and scale effects, but at same time produce a row shifted log-polar image, which is then passed to an <it>adaptive row shift invariant wavelet packet transform</it> to eliminate the row shift effects. So, the output wavelet coefficients are rotation and scale invariant. The adaptive row shift invariant wavelet packet transform is quite efficient with only O (n \cdot log n) complexity. A feature vector of the most dominant log-polar wavelet energy signatures extracted from each subband of wavelet coefficients is constructed for rotation and scale invariant texture classification. In the experiments, we employed a Mahalanobis classifier to classify a set of 25 distinct natural textures selected from the Brodatz album. The experimental results, based on different testing data sets for images with different orientations and scales, show that the proposed classification scheme using log-polar wavelet signatures outperforms two other texture classification methods, its overall accuracy rate for joint rotation and scale invariance being 90.8 percent, demonstrating that the extracted energy signatures are effective rotation and scale invariant features. Concerning its robustness to noise, the classification scheme also performs better than the other methods.</p>
Rotation and scale invariance, texture classification, shift invariant wavelet packet transform, log-polar transform.
C. Pun and M. Lee, "Log-Polar Wavelet Energy Signatures for Rotation and Scale Invariant Texture Classification," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 25, no. , pp. 590-603, 2003.