Abstract
Kernel canonical correlation analysis (KCCA) with autocorrelation kernels is applied to invariant texture classification. The autocorrelation kernels are the inner products of the autocorrelation functions of original data and effectively calculated with the cross-correlation functions. Classification experiment shows the autocorrelation kernels perform better than the linear and Gaussian kernels in KCCA. Further, it is shown that the generalization ability is degraded as the order of the autocorrelation kernels increases, since relative values of the kernels of different data tend to zero.
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References
Ruiz, A., López-de-Teruel, P.E.: Nonlinear Kernel-Based Statistical Pattern Analysis. IEEE Trans. Neural Networks 12, 16–32 (2001)
Müller, K.-R., et al.: An Introduction to Kernel-Based Learning Algorithms. IEEE Trans. Neural Networks 12, 181–201 (2001)
Kim, K.I., et al.: Support Vector Machines for Texture Classification. IEEE Trans. Pattern Analysis and Machine Intelligence. 24, 1542–1550 (2002)
Kim, K.I., et al.: Kernel Principal Component Analysis for Texture Classification. IEEE Signal Processing Letters 8, 39–41 (2001)
Lai, P.L., Fyfe, C.: Kernel and Nonlinear Canonical Correlation Analysis. Int. J. Neural Systems 10, 365–377 (2000)
Akaho, S.: A Kernel Method for Canonical Correlation Analysis. In: Proc. Int. Meeting on Psychometric Society (IMPS 2001) (2001)
Bach, F.R., Jordan, M.I.: Kernel Independent Component Analysis. J. Machine Learning Research 3, 1–48 (2002)
Kuss, M., Graepel, T.: The Geometry of Kernel Canonical Correlation Analysis. Technical Report 108. Max Plank Institute for Biological Cybernetics (2002)
Melzer, T., Reiter, M., Bischof, H.: Appearance Models Based on Kernel Canonical Correlation Analysis. Pattern Recognition 36, 1961–1971 (2003)
Hardoon, D.R., Szedmak, S., Shawe-Taylor, J.: Canonical Correlation Analysis; An Overview with Application to Learning Methods. Technical Report CSD-TR-03-02. Dept. of Computer Science, University of London (2003)
Popovici, V., Thiran, J.P.: Higher Order Autocorrelations for Pattern Classification. In: Proc. IEEE 2001 International Conference on Image Processing (ICIP 2001), pp. 724–727 (2001)
McLaughlin, J.A., Raviv, J.: Nth-Order Autocorrelations in Pattern Recognition. Information and Control 12, 121–142 (1968)
Horikawa, Y.: Comparison of Support Vector Machines with Autocorrelation Kernels for Invariant Texture Classification. Submitted to 17th Int. Conf. Pattern Recognition (ICPR 2004) (2004) (to appear)
Schölkopf, B., Smola, A., Müller, K.-R.: Nonlinear Component Analysis as a Kernel Eigenvalue Problem. Neural Computation 10, 1299–1319 (1998)
Brodatz, P.: Textures: A Photographic Album for Artists and Designers. Dover, New York (1966)
Randen, T., Husøy, J.H.: Filtering for Texture Classification: A Comparative Study. IEEE Trans. Pattern Analysis and Machine Intelligence 21, 291–310 (1999)
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Horikawa, Y. (2004). Use of Autocorrelation Kernels in Kernel Canonical Correlation Analysis for Texture Classification. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds) Neural Information Processing. ICONIP 2004. Lecture Notes in Computer Science, vol 3316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30499-9_192
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DOI: https://doi.org/10.1007/978-3-540-30499-9_192
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