A new Gabor filter based kernel for texture classification with SVM

The performance of Support Vector Machines (SVMs) is highly dependent on the choice of a kernel function suited to the problem at hand. In particular, the kernel implicitly performs a feature selection which is the most important stage in any texture classification algorithm. In this work a new Gabor filter based kernel for texture classification with SVMs is proposed. The proposed kernel function is based on a Gabor filter decomposition and exploiting linear predictive coding (LPC) in each subband, and exploiting a filter selection method to choose the best filters. The proposed texture classification method is evaluated using several texture samples, and compared with recently published methods. The comprehensive evaluation of the proposed method shows significant improvement in classification error rate.