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Log-Polar Wavelet Energy Signatures for Rotation and Scale Invariant Texture Classification
May 2003 (vol. 25 no. 5)
pp. 590-603

Abstract—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 log-polar transform to eliminate the rotation and scale effects, but at same time produce a row shifted log-polar image, which is then passed to an adaptive row shift invariant wavelet packet transform 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.

[1] M. Tuceryan and A.K. Jain, “Texture Analysis,” Handbook Pattern Recognition and Computer Vision, C.H. Chen, L.F. Pau, and P.S.P. Wang, eds., Singapore: World Scientific, pp. 235-276, 1993.
[2] R.W. Conners and C.A. Harlow, “A Theoretical Comparison of Texture Algorithms,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 2, pp. 204-222, May 1980.
[3] A.C. Bovik,M. Clark,, and W.S. Geisler,“Multichannel texture analysis using localized spatial filters,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 12, pp. 55-73, 1990.
[4] A. Teuner, O. Pichler, and B.J. Hosticka, "Unsupervised Texture Segmentation of Images Using Tuned Matched Gabor Filters," IEEE Trans. Image Processing, vol. 4, pp. 863-870, June 1995.
[5] T. Chang and C.-C.J. Kuo, "Texture Analysis and Classification With Tree-Structured Wavelet Transform," IEEE Trans. Image Processing, vol. 2, no. 4, pp. 429-441, Oct. 1993.
[6] A. Laine and J. Fan, “Texture Classification by Wavelet Packet Signature,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 15, no. 11, pp. 1,186-1,191, Nov. 1993.
[7] M. Unser, Texture Classification and Segmentation Using Wavelet Frames IEEE Trans. Image Processing, vol. 4, no. 11, pp. 1549-1560, Nov. 1995.
[8] R.L. Kashyap and A. Khotanzad, “A Model-Based Method for Rotation Invariant Texture Classification,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 8, no. 7, pp. 472-481, July 1986.
[9] M. Leung and A.M. Peterson, Scale and Rotation Invariant Texture Classification Proc. Int'l Conf. Acoustics, Speech, and Signal Processing, pp. 461-165, 1991.
[10] F.S. Cohen, Z.G. Fan, and M.A. Patel, Classification of Rotated and Scaled Textured Images Using Gaussian Markov Random Field Models IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 13, no. 2, pp. 192-202, Feb. 1991.
[11] J. You and H.A. Cohen, “Classification and Segmentation of Rotated and Scaled Textured Images Using Texture‘Tuned’Masks,” Pattern Recognition, vol. 26, pp. 245-258, 1993.
[12] G.M. Haley and B.S. Manjunath, "Rotation Invariant Texture Classification Using Modified Gabor Filters," Proc. IEEE ICIP95, pp. 262-265, 1995.
[13] S.R. Fountain and T.N. Tan, "Extraction of Noise Robust and Rotation Invariant Texture Features via Multichannel Filtering," Proc. IEEE Int'l Conf. Image Processing, vol. 3, pp. 197-200, Oct. 1997.
[14] S.R. Fountain, T.N. Tan, and K.D. Baker, “A Comparative Study of Rotation Invariant Classification and Retrieval of Texture Images,” Proc. Ninth British Machine Vision Conf., pp. 266-275, Sept. 1998.
[15] R. Porter and N. Canagarajah, Robust Rotation-Invariant Texture Classification: Wavelet, Gabor Filter and GMRF Based Schemes IEE Proc. Conf. Vision, Image, and Signal Processing, vol. 144, no. 3, pp. 180-188, 1997.
[16] J. Chen and A. Kundu, “Rotation and Gray Scale Transform Invariant Texture Identification Using Wavelet Decomposition and Hidden Markov Model,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 16, no. 2, pp. 208-214, Feb. 1994.
[17] W.-R. Wu and S.-C. Wei, "Rotation and Gray-Scale Transformation-Invariant Texture Classification Using Spiral Resampling, Subband Decomposition, and Hidden Markov Model," IEEE Trans. Image Processing, vol. 5, no. 10, pp. 1,423-1,434, Oct. 1996.
[18] G.M. Haley and B.S. Manjunath, Rotation-Invariant Texture Classification Using a Complete Space-Frequency Model IEEE Trans. Image Processing, vol. 8, no. 2, pp. 255-269, 1999.
[19] S.-D. Kim and S. Udpa, “Texture Classification Using Rotated Wavelet Filter,” IEEE Trans. Systems, Man, and Cybernetics, Part A, vol. 30, no. 6, pp. 847-852, Nov. 2000.
[20] S.G. Mallat,“A theory for multiresolution signal decomposition: The wavelet representation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 11, no. 7, pp. 674-693, 1989.
[21] G. Beylkin, “On the Representation of Operators in Bases of Compactly Supported Wavelets,” SIAM J. Numerical Analysis, vol. 6, no. 6, pp. 1716-1740, Dec. 1992.
[22] R.R. Coifman and D.L. Donoho, “Translation-Invariant De-Noising,” Wavelet and Statistics, Lecture Notes in Statistics, A. Antoniadis and G. Oppenheim, ed., Springer-Verlag, pp. 125-150, 1995.
[23] J. Liang and T.W. Parks, A Translation-Invariant Wavelet Representation Algorithm with Applications IEEE Trans. Signal Processing, vol. 44, pp. 225-232, Feb. 1996.
[24] J.C. Pesquet, H. Hrim, and H. Carfantan, Time-Invariant Orthonormal Wavelet Representations IEEE Trans. Signal Processing, vol. 44, pp. 1964-1970, Aug. 1996.
[25] I. Cohen, S. Raz, and D. Malah, “Orthonormal Shift-Invariant Wavelet Packet Decomposition and Representation,” Signal Processing, vol. 57, no. 3, pp. 251-270, Mar. 1997.
[26] R.R. Coifman and M.V. Wickerhauser, "Entropy Based Algorithms for Best Basis Selection," IEEE Trans. Information Theory, vol. 38, pp. 713-718, 1992.
[27] I. Daubechies, “Orthonormal Bases of Compactly Supported Wavelets,” Comm. Pure and Applied Math., vol. 41, pp. 909-996, Nov. 1988.
[28] R.R. Coifman and D.L. Donoho, “Translation-Invariant De-Noising,” Wavelet and Statistics, Lecture Notes in Statistics, A. Antoniadis and G. Oppenheim, ed., Springer-Verlag, pp. 125-150, 1995.
[29] I. Daubechies,“Ten lectures on wavelets,” SIAM CBMS-61, 1992.
[30] G. Beylkin, “On the Representation of Operators in Bases of Compactly Supported Wavelets,” SIAM J. Numerical Analysis, vol. 6, no. 6, pp. 1716-1740, Dec. 1992.
[31] P. Brodatz, Texture: A Photographic Album for Artists and Designers. New York: Dover, 1966.
[32] R.J. Schalkoff, Pattern Recognition: Statistical, Structural and Neural Approaches.New York: John Wiley and Sons, 1992.

Index Terms:
Rotation and scale invariance, texture classification, shift invariant wavelet packet transform, log-polar transform.
Citation:
Chi-Man Pun, Moon-Chuen Lee, "Log-Polar Wavelet Energy Signatures for Rotation and Scale Invariant Texture Classification," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 5, pp. 590-603, May 2003, doi:10.1109/TPAMI.2003.1195993
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