Kernel Regularized Data Uncertainty for Action Recognition

The traditional data uncertainty (DU) classifier fails to encode the importance of each sample for solving the minimum problem. Moreover, it considers only linear information for classification. To overcome these, we propose four classifiers for action recognition. They are called regularized DU (RDU) classifier, RDU coefficient (RDUC) classifier, kernel RDU (KRDU) classifier, and kernel RDUC (KRDUC) classifier, respectively. Extensive experiments on four benchmark action databases demonstrate that the proposed four classifiers achieve better recognition rates than the traditional DU classifier and several state-of-the-art methods. Moreover, the computation costs of the KRDU and KRDUC classifiers are much less than that of the DU classifier.