Optimized space frequency kernel for texture classification

The performance of the support vector machine (SVM) algorithm is highly dependent on the choice of the kernel function suited to the problem at hand. In a support vector machine algorithm feature selection is implicitly performed by kernel function. On the other hand, feature selection is the most important stage in any texture classification algorithm. In this work, the performance of SVM is improved by choosing an optimized space-frequency (SFR) kernel function. The proposed method is evaluated in a two-texture and multi-texture problems. The results are compared with the original SVM and other recently published texture classification methods. The comparison shows a significant improvement in error rates. Improvement of more than 40% in compare with original SVM and about 60% in compare with logical operators (LO) and wavelet co-occurrence features (WCOF) are obtained.