A comparison of support vector machine with maximum likelihood classification algorithms on texture features

A study is presented concerning the performance of support vector machines (SVMs) and maximum likelihood (ML) classification algorithms on texture features. A novel multivariate modeling method--partial least square regression (PLSR) is applied to get orthogonal components from texture spectrum features. Three texture features,together with the above components, are used in Brodatz texture classification tests. The experiments show: 1) SVMs perform better than ML classifier. 2) PLSR can improve the texture spectrum-based features discrimination ability with ML classifier. 3) Not one of the texture features performs best on all test images.

[1]  K. Kameyama,et al.  Texture classification by support vector machines with kernels for higher-order Gabor filtering , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[2]  Y. Horikawa.  Comparison of support vector machines with autocorrelation kernels for invariant texture classification , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[3]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[4]  Peng Gong,et al.  A comparison of reduced texture spectrum approach with gray level reduction schemes for land-use classification with sampled IKONOS imagery , 2001, 2001 International Conferences on Info-Tech and Info-Net. Proceedings (Cat. No.01EX479).

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

[6]  R. Rosipal,et al.  Kernel Partial Least Squares for Nonlinear Regression and Discrimination , 2002 .

[7]  Martino Pesaresi,et al.  Texture Analysis for Urban Pattern Recognition Using Fine-resolution Panchromatic Satellite Imagery , 2000 .

[8]  Dong-Chen He,et al.  Texture Unit, Texture Spectrum, And Texture Analysis , 1990 .

[9]  Bing-Yu Sun,et al.  Texture classification based on support vector machine and wavelet transform , 2004, Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788).

[10]  Peng Xu,et al.  Texture classification using optimized support vector machines , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[11]  Victor L. Brailovsky,et al.  On global, local, mixed and neighborhood kernels for support vector machines , 1999, Pattern Recognit. Lett..