Sparse kernel learning for image set classification

Abstract

No single universal image set representation can efficiently encode all types of image set variations. In the absence of expensive validation data, automatically ranking representations with respect to performance is a challenging task. We propose a sparse kernel learning algorithm for automatic selection and integration of the most discriminative subset of kernels derived from different image set representations. By optimizing a sparse linear discriminant analysis criterion, we learn a unified kernel from the linear combination of the best kernels only. Kernel discriminant analysis is then performed on the unified kernel. Experiments on four standard datasets show that the proposed algorithm outperforms current state-of-the-art image set classification and kernel learning algorithms.