Heterogeneous Discriminant Analysis for Cross-View Action Recognition

We propose an approach of cross-view action recognition, in which the samples from different views are represented by heterogeneous features with different dimensions. Inspired by linear discriminant analysis (LDA), we introduce a discriminative common feature space to bridge the source and target views. Two different projection matrices are learned to respectively map the data from two different views into the common space by simultaneously maximizing the similarity of intra-class samples, minimizing the similarity of inter-class samples, and reducing the mismatch between data distributions of two views. Our method is neither restricted to the corresponding action instances in the two views nor restricted to a specific type of feature. We evaluate our approach on the IXMAS multi-view dataset and the experimental results demonstrate its effectiveness.