Comparison of support vector machines with autocorrelation kernels for invariant texture classification

Support vector machines (SVMs) with autocorrelation kernels are applied to texture classification invariant to similarity transformations and noise. The inner product of autocorrelation functions of an arbitrary order is effectively calculated through the 2nd-order crosscorrelation of original data. Texture classification experiments show that higher performance of SVMs is achieved by exploiting the autocorrelation kernels.

[1]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[2]  Yo Horikawa.  Bispectrum-based feature of 2D and 3D images invariant to similarity transformations , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[3]  Hang Joon Kim,et al.  Support Vector Machines for Texture Classification , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  John A. McLaughlin,et al.  Nth-Order Autocorrelations in Pattern Recognition , 1968, Inf. Control..

[5]  Trygve Randen,et al.  Filtering for Texture Classification: A Comparative Study , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Tieniu Tan,et al.  Brief review of invariant texture analysis methods , 2002, Pattern Recognit..

[7]  Samy Bengio,et al.  SVMTorch: Support Vector Machines for Large-Scale Regression Problems , 2001, J. Mach. Learn. Res..

[8]  Jean-Philippe Thiran,et al.  Higher order autocorrelations for pattern classification , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[9]  Vittorio Murino,et al.  Noisy texture classification: A higher-order statistics approach , 1998, Pattern Recognit..

[10]  Yo Horikawa.  Invariant noisy texture classification with bispectrum-based features from projections , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[11]  Yo Horikawa.  Pattern recognition with invariance to similarity transformations based on the third-order correlation , 1996, Proceedings of 13th International Conference on Pattern Recognition.