A novel robust kernel for applications to images

Robustness is an essential issue to computer vision and pattern recognition in developing multimedia applications. In this work, we present a robust kernel approach that is highly robust against random noises and intra-class deformations. By incorporating the robust error function used in robust statistics together with a deformation-invariant distance measure, the derived robust kernel is shown to be insensitive to the influence of outliers and robust to intra-class deformations. In the experiments, we justify our robust kernel with different kernel machines with applications to handwritten digit recognition and data visualization on the USPS database.