This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Support Vector Machines for Texture Classification
November 2002 (vol. 24 no. 11)
pp. 1542-1550

Abstract—This paper investigates the application of support vector machines (SVMs) in texture classification. Instead of relying on an external feature extractor, the SVM receives the gray-level values of the raw pixels, as SVMs can generalize well even in high-dimensional spaces. Furthermore, it is shown that SVMs can incorporate conventional texture feature extraction methods within their own architecture, while also providing solutions to problems inherent in these methods. One-against-others decomposition is adopted to apply binary SVMs to multitexture classification, plus a neural network is used as an arbitrator to make final classifications from several one-against-others SVM outputs. Experimental results demonstrate the effectiveness of SVMs in texture classification.

[1] H. Greenspan, R. Goodman, R. Chellappa, and C.H. Anderson, “Learning Texture Discrimination Rules in a Multiresolution System,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 16, no. 9, pp. 894-900, Sept. 1994.
[2] A.K. Muhamad and F. Deravi, “Neural Networks for the Classification of Image Texture,” Eng. Applications of Artificial Intelligence, vol. 7, pp. 381-393, 1994.
[3] Y.Q. Chen, M.S. Nixon, and D.W. Thomas, “On Texture Classification,” Int'l J. Systems Science, vol. 28, no. 7, pp. 669-682, 1997.
[4] 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.
[5] R. Haralick, K. Shangmugam, and L. Dinstein, “Textural Features for Image Classification,” IEEE Trans. Systems, Man, and Cybernetics, vol. 3, pp. 610-621, 1973.
[6] P. Diaconis and D. Freedman, “On the Statistics of Vision: The Julesz Conjecture,” J. Math. Psychology, vol. 24, 1981.
[7] S.Z. Li, Markov Random Field Modeling in Computer Vision. New York: Springer-Verlag, 1995.
[8] H.J. Kim, E.Y. Kim, J.W. Kim, and S.H. Park, “MRF Model Based Image Segmentation Using Hierarchical Distributed Genetic Algorithm,” IEE Electronics Letters, vol. 34, no. 25, pp. 1394-1395, 1998.
[9] T. Randen and J.H. Husoy, “Filtering for Texture Classification: A Comparative Study,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 21, no. 4, pp. 291-310, Apr. 1999.
[10] A.K. Jain and F. Farrokhnia, “Unsupervised Texture Segmentation Using Gabor Filters,” Pattern Recognition, vol. 24, no. 12, pp. 1167-1186, 1991.
[11] C.S. Lu, P.C. Chung, and C.F. Chen, “Unsupervised Texture Segmentation via Wavelet Transform,” Pattern Recognition, vol. 30, no. 5, pp. 729-742, 1997.
[12] A.K. Jain and K. Karu, “Learning Texture Discrimination Masks,” IEEE Trans. Pattern Analysis Machine Intelligence, vol. 18, no. 2, pp. 195-205, Feb. 1996.
[13] V.N. Vapnik, Statistical Learning Theory, John Wiley&Sons, 1998.
[14] B. Scholkopf, K. Sung, C.J.C. Burges, and F. Girosi, Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers IEEE Trans. Signal Processing, vol. 45, no. 11, pp. 2758-2765, 1999.
[15] S. Haykin, Neural Network—A Comprehensive Foundation, second ed. Prentice Hall, 1999.
[16] C.J.C. Burges, “A Tutorial on Support Vector Machines for Pattern Recognition,” Data Mining and Knowledge Discovery, vol. 2, no. 2, pp. 1-47, 1998.
[17] T.M. Cover, “Geometrical and Statistical Properties of Systems of Linear Inequalities with Applications in Pattern Recognition,” IEEE Trans. Electronic Computers, vol. 14, pp. 326-334, 1965.
[18] K.I. Laws, “Textured Image Segmentation,” PhD thesis, Univ. of Southern Calif., 1980.
[19] D.F. Dunn, W.E. Higgins, and J. Wakeley, “Determining Gabor Filter Parameters for Texture Segmentation,” Proc. SPIE Intelligence Robots and Computer Vision XI, pp. 51-63, 1992.
[20] S.C. Zhu, Y. Wu, and D. Mumford, “Filters, Random Fields and Maximum Entropy (FRAME)—Towards a Unified Theory for Texture Modeling,” Int'l J. Computer Vision, vol. 27, no. 2, 1998.
[21] B. Schölkopf, C. Burges, and V. Vapnik, “Extracting Support Data for a Given Task,” Proc. Int'l Conf. Knowledge Discovery&Data Mining, pp. 252-257, 1995.
[22] B. Schölkopf, “Support Vector Learning,” PhD thesis, Munich: Oldenbourg Verlag, 1997.
[23] J.-L. Chen and A. Kundu, “Unsupervised Texture Segmentation Using Multichannel Decomposition and Hidden Markov Models,” IEEE Trans. Image Processing, vol. 4, no. 5, pp. 603-619, 1995.
[24] E. Mayoraz and E. Alpaydin, “Support Vector Machines for Multi-Class Classification,” Technical Report IDIAP-PR 98-06, Dalle Molle Inst. for Perceptual Artificial Intelligence, 1998.
[25] P. Brodatz, Textures: A Photographic Album for Artists and Designers. New York: Dover, 1966.
[26] MIT Vision and Modeling Group. 1998.
[27] C.-W. Hsu and C.-J. Lin, “A Comparison on Methods for Multi-Class Support Vector Machines,” technical report, Dept. of Computer Science and Information Eng., Nat'l Taiwan Univ., 2001.
[28] K.I. Laws, “Rapid Texture Identification,” Proc. SPIE Conf. Image Processing for Missile Guidance, pp. 376-380, 1980.
[29] J. Mercer, “Functions of Positive and Negative Type, and Their Connection with the Theory of Integral Equations,” Trans. London Philosophical Soc. (A), vol. 209, pp. 415-446, 1909.
[30] A.L. Yuille, J. Coughlan, S.C. Zhu, and Y. Wu, “Order Parameters for Minimax Entropy Distributions: When Does High Level Knowledge Help?” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 558-565, 2000.

Index Terms:
Support vector machines, texture analysis, pattern classification, machine learning, feature extraction.
Citation:
Kwang In Kim, Keechul Jung, Se Hyun Park, Hang Joon Kim, "Support Vector Machines for Texture Classification," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 11, pp. 1542-1550, Nov. 2002, doi:10.1109/TPAMI.2002.1046177
Usage of this product signifies your acceptance of the Terms of Use.