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Texture Classification Using Windowed Fourier Filters
February 1997 (vol. 19 no. 2)
pp. 148-153

Abstract—We define a distance between textures for texture classification from texture features based on windowed Fourier filters. The definition of the distance relies on an interpretation of our texture attributes in terms of spectral density when the texture can be considered as a Gaussian random field. The distance between textures is then defined as a symmetrized Kullback distance which is a simple function of the attributes and does not require any normalization. An experimental analysis using Gabor filters, and in particular a comparison to quadratic distances, shows the efficiency and robustness of the method.

[1] R. Azencott, "Image Analysis and Markov Fields," Proc. Int'l Conf. Ind. and Appl. Math, SIAM,Paris, 1987. SIAM Philadelphia, 1988.
[2] R. Azencott and D. Dacunha-Castelle, Series of Irregular Observation-Forecasting and Model Building. Springer-Verlag, 1986.
[3] J. Besag, "On the Statistical Analysis of Dirty Pictures," J. Royal Statistical Society, vol. B.48, pp. 259-302, 1986.
[4] 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.
[5] R. Chellappa and S. Chatterjee, "Classification of Textures Using Gaussian Markov Random Fields," IEEE Trans. Acoustics, Speech, and Signal Processing, vol. 33, pp. 959-963, 1985.
[6] R. Chellappa, S. Chatterjee, and R. Bagdazian, "Texture Synthesis and Compression Using Gaussian-Markov Random Field Models," IEEE Trans. Syst., Man, Cybern., vol. 15, pp. 298-303, March/April 1985.
[7] C.H. Chen, "A Study of Texture Classification Using Spectral Features," Proc. Int'l Conf. Pattern Recognition, pp. 1,074-1,077,Munich, West Germany, 1982.
[8] J.-L. Chen and A. Kunda, "Automatic Unsupervised Texture Segmentation Using Hidden Markov Model," Proc. Int'l Conf. Acoust., Speech, Signal Processing, pp. v.21-v.24,Minneapolis, Apr. 1993.
[9] 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.
[10] G.R. Cross and A.K. Jain, "Markov Random Field Texture Models," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 5, no. 1, pp. 25-39, Jan. 1983.
[11] J.G. Daugman, “Complete Discrete 2D Gabor Transforms by Neural Networks for Image Analysis and Compression,” IEEE Trans. Acoustics, Speech, and Signal Processing, vol. 36, no. 7, 1988.
[12] O.D. Faugeras and W.K. Pratt, "Decorrelation Methods for Texture Feature Extraction," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 2, no. 7, pp. 323-332, July 1980.
[13] A. Gagalowicz and C. Graffigne, "Blind Texture Segmentation," Proc. Ninth Int'l Conf. Pattern Recognition,Rome, 1988.
[14] M.M. Galloway, "Texture Analysis Using Gray Level Run Lengths," Computer Graphics and Image Processing, vol. 4, pp. 172-179, 1975.
[15] D. Geman and S. Geman, "Stochastic Relaxation, Gibbs Distribution, and the Bayesian Restoration of Images," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 6, pp. 721-741, Nov. 1984.
[16] R.M. Haralick, "Statistical and Structural Approaches to Texture," Proc. IEEE, vol. 67, pp. 786-804, May 1979.
[17] R.M. Haralick, K. Shanmugam, and I. Dinstein, "Textural Features for Image Classification," IEEE Trans. Syst., Man, Cybern., vol. 3, pp. 610-621, Nov. 1973.
[18] A.K. Jain and F. Farrokhnia, "Unsupervised Texture Segmentation Using Gabor Filters," Pattern Recognition, vol. 24, no.12, pp. 1,167-1,186, 1991.
[19] R.L. Kashyap and R. Chellappa,“Estimation and choice of neighbors in spatial-interaction models of images,” IEEE Trans. Information Theory, vol. 29, no. 1, pp. 60-72, Jan. 1983.
[20] R.L. Kashyap and A. Khotanzad, "A Model-Based Method for Rotation Invariant Texture Classification," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 8, pp. 472-481, July 1986.
[21] 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.
[22] M. Porat and Y. Zeevi, "The Generalized Gabor Scheme of Image Representation in Biological and Machine Vision," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 10, no. 4, pp. 452-468, 1988.
[23] T.R. Reed and J.M.H. du Buf, "A Review of Recent Texture Segmentation and Feature Extraction Techniques," CVGIP: Image Understanding, vol. 57, pp. 359-372, May 1993.
[24] M. Rosenblatt, Stationary Sequences and Random Fields. Birkhäuser, 1985.
[25] H.L. Royden, Real Analysis. The Macmillan Company, 1963.
[26] J.-P. Wang, "Multiscale Markov Fields: Applications to the Segmentation of Textured Images and Film Fusion," (in French) PhD dissertation, Paris-Sud University, 1994.
[27] J.S. Weszka, C.R. Dyer, and A. Rosenfeld, "A Comparative Study of Texture Measures for Terrain Classification," IEEE Trans. Syst., Man, Cybern., vol. 6, pp. 269-285, Apr. 1976.

Index Terms:
Texture attributes and classification, windowed Fourier filters, Gabor filters, Gaussian random fields, spectral density, Kullback distance, distance between textures, segmentation of textured images.
Citation:
Robert Azencott, Jia-Ping Wang, Laurent Younes, "Texture Classification Using Windowed Fourier Filters," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 2, pp. 148-153, Feb. 1997, doi:10.1109/34.574796
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