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Texture Classification by Wavelet Packet Signatures
November 1993 (vol. 15 no. 11)
pp. 1186-1191

This correspondence introduces a new approach to characterize textures at multiple scales. The performance of wavelet packet spaces are measured in terms of sensitivity and selectivity for the classification of twenty-five natural textures. Both energy and entropy metrics were computed for each wavelet packet and incorporated into distinct scale space representations, where each wavelet packet (channel) reflected a specific scale and orientation sensitivity. Wavelet packet representations for twenty-five natural textures were classified without error by a simple two-layer network classifier. An analyzing function of large regularity (D/sub 20/) was shown to be slightly more efficient in representation and discrimination than a similar function with fewer vanishing moments (D/sub 6/) In addition, energy representations computed from the standard wavelet decomposition alone (17 features) provided classification without error for the twenty-five textures included in our study. The reliability exhibited by texture signatures based on wavelet packets analysis suggest that the multiresolution properties of such transforms are beneficial for accomplishing segmentation, classification and subtle discrimination of texture.

[1] R. M. Haralick, "Statistical and structural approaches to texture,"Proc. IEEE, vol. 67, pp. 786-804, 1979.
[2] L. S. Davis, "Image texture analysis techniques survey," inDigital Image Processing, J. C. Simon and R. M. Haralick, Eds. Dordrecht, The Netherlands: D. Reidel, 1980, pp. 189-201.
[3] R. Bajcsy, "Computer description of textured surfaces," inProc. 3rd Int. Joint Conf. Artificial Intell., Aug. 1973, 572-579.
[4] C. H. Chen, "A study of texture classification using spectral features," inProc. IEEE 6th Int. Conf. Pattern Recongnit., Munich, Germany, Oct. 19-22, 1982, pp. 1074-1077.
[5] V. L. Vickers and J. W. Modestino, "A maximum likelihood approach to texture classification,"IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-4, pp. 61-68, 1982.
[6] R. Wilson and M. Spann,Image Segmentation and Uncertainty. New York: Wiley (Research Studies Press), 1987.
[7] M. Unser, "Sum and difference histograms for texture classification,"IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-8, pp. 118-125, 1986.
[8] M. Unser, "Local linear transforms for texture measurements,"Signal Processing, vol. 11, pp. 61-79, 1986.
[9] M. Unser and M. Eden, "Multiresolution feature extraction and selection for texture segmentation,"IEEE Trans. Pattern Anal. Machine Intell., vol. 11, pp. 717-728, 1989.
[10] R. M. Haralick, K. Shanmugam, and I. Dinstein, "Textural features for image classification,"IEEE Trans. Syst., Man, Cybern., vol. SMC-3, pp. 610-621, 1973.
[11] J. S. Weszka, C. R. Dyer, and A. Rosenfield, "Comparative study of texture measures for terrain classification,"IEEE Trans. Syst., Man, Cybern., vol. SMC-6, pp. 269-285, 1976.
[12] R. L. Kashyap and A. Khotanzad, "A Model-Based Method for Rotation Invariant Texture Classification,"IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-8, pp. 472-481, July 1986.
[13] F. S. Cohen, Z. Fan, and M. A. Patel, "Classification of rotated and scaled textured images using Gaussian Markov random field models,"IEEE Trans. Pattern Anal. Machine Intell., vol. 13, pp. 192-202, 1991.
[14] B. Julesz, E. N. Gilbert, J. D. Victor, "Visual discrimination of textures with identical third-order statistics,"Biol. Cybern., vol. 31, pp. 137-140, 1978.
[15] B. Julesz, "A theory of preattentive texture discrimination based on first-order statistics of textons,"Biol. Cybern., vol. 41, pp. 131-138, 1981.
[16] R. De Valois, and K. De Valois,Spatial Vision. New York: Oxford Univ. Press, 1988.
[17] S. Marcelja, "Mathematical description of the responses of simple cortial cells,"J. Opt. Soc. Am., vol. 70, pp. 1297-1300, Nov. 1980.
[18] J. Beck, A. Sutter, and R. Ivry, "Spatial frequency channels and perceptual grouping in texture segregation,"Comput. Vision, Graphics, Image Processing, vol. 37, pp. 299-325, 1987.
[19] A. Gagalowicz, "A new method for texture fields synthesis: Some applications to the study of human vision,"IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-3, pp. 520-533, 1982.
[20] M. R. Turner, "Texture discrimination by Gabor functions,"Biolog. Cybern., vol. 55, pp. 71-82, 1986.
[21] A. C. Bovik and M. Clark, "Multichannel texture analysis using localized spatial filters,"IEEE Trans. Pattern Anal. Machine Intell., vol. 12, pp. 55-73, 1990.
[22] D. W. Paglieroni, "Distance transforms: Properties and machine vision applications,"Comput. Vision Graphics Image Processing: Graphical Models Image Processing, vol. 54, no. 1, pp. 56-74, 1992.
[23] A. C. Bovik, N. Gopal, T. Emmoth,and A. Pestrepo, "Localized measurement of emergent image frequencies by Gabor wavelets,"IEEE Trans. Inform. Theory, vol. 38, pp. 691-712, Mar. 1992.
[24] A. K. Jain and F. Farrokhnia, "Unsupervised texture segmentation using Gabor filters,"Pattern Recognit., vol. 24, pp. 1167-1186, 1991.
[25] A. K. Jain and S. Bhattacharjee, "Text segmentation using Gabor filters for automatic document processing,"Machine Vision and Applicat., vol. 5, pp. 169-184, 1992.
[26] B. S. Manjunath and R. Chellappa, "A computational approach to boundary detection,"Proc. CVPR, 1991, pp. 358-363.
[27] T. R. Reed and H. Wechsler, "Segmentation of textured images and gestalt organization using spatial/spatial-frequency representations,"IEEE Trans. Pattern Anal. Machine Intell., vol. 12, pp. 1-12, Jan. 1990.
[28] J. R. Bergen and M. S. Landy, "Computational modeling of visual texture segregation," inComputational Models of Visual Processing, M. S. Landy and J. A. Movshon, Eds. Cambridge, MA: The MIT Press, 1991, pp. 472-481.
[29] P. H. Carter, "Texture discrimination using wavelets," inSPIE applications of digital image processing XIV, vol. 1567, pp. 432-438, 1991.
[30] S. Mallat, "A theory for multiresolution signal decomposition: The wavelet representation,"IEEE Trans. Pattern Anal. Machine Intell., vol. 11, pp. 674-693, 1989.
[31] R. R. Coifman and Y. Meyer, "Orthonormal wave packet bases," preprint, Yale Univ., Aug., 1989.
[32] R. R. Coifman and M. V. Wickerhauser, "Best-adapted wavelet packet bases," preprint, Yale Univ., Feb. 1990.
[33] R. R. Coifman and M. V. Wickerhauser, "Entropy-based algorithms for best basis selection,"IEEE Trans. Inform. Theory, vol. 38, pp. 713-718, May 1992.
[34] I. Daubechies, "Orthonormal bases of compactly supported wavelets,"Commun. Pure Appl. Math., vol. XLI, pp. 909-996, 1988.
[35] R. E. Crochiere and L. R. Rabiner,Multirate digital signal processing. Englewood Cliffs, NJ: Prentice-Hall, 1983.
[36] R. R. Coifmanet al., "Signal processing and compression with wave packets," preprint, Yale Univ.
[37] P. Brodatz,Textures--A Photographic Album for Artists and Designers. New York: Dover, 1966.
[38] D. E. Rumelhart and J. L. McClelland,Parallel Distributed Processing. Cambridge, MA: MIT Press, 1986.
[39] B. L. Kalman, "Super linear learning in back propagation neural nets," Wucs-90-21, Washington Univ., St. Louis, MO, 1990.
[40] J. T. Tou and R. C. Gonzalez,Pattern Recognition Principles. Reading, MA: Addison-Wesley, 1974; second printing 1977.

Index Terms:
texture classification; wavelet packet signatures; scale-independence; wavelet packet spaces; sensitivity; selectivity; energy metrics; entropy metrics; scale space representations; scale sensitivity; orientation sensitivity; two-layer network classifier; feature extraction; feedforward neural nets; image recognition; wavelet transforms
A. Laine, J. Fan, "Texture Classification by Wavelet Packet Signatures," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no. 11, pp. 1186-1191, Nov. 1993, doi:10.1109/34.244679
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