Tensor discriminant analysis on grassmann manifold with application to video based human action recognition

Representing videos as linear subspaces on Grassmann manifolds has made great strides in action recognition problems. Recent studies have explored the convenience of discriminant analysis by making use of Grassmann kernels. However, traditional methods rely on the matrix representation of videos based on the temporal dimension and suffer from not considering the two spatial dimensions. To overcome this problem, we keep the natural form of videos by representing video inputs as multidimensional arrays known as tensors and propose a tensor discriminant analysis approach on Grassmannian manifolds. Because matrix algebra does not handle tensor data, we introduce a new Grassmann projection kernel based on the tensor-tensor decomposition and product. Experiments with human action databases show that the proposed method performs well compared with the state-of-the-art algorithms.