A Tensor-Driven Temporal Correlation Model for Video Sequence Classification

Abstract

The task of video sequence classification plays a critical role in the development of computer vision. Considering this fact, this letter proposes a novel tensor decomposition method called tensor-driven temporal correlation in which general tensors are used as input for video sequence classification. Because distortion and redundancy may exist in the tensor representations of video sequences, we project the original tensor into subspaces spanned by spatial basis matrices in the proposed formulation. Moreover, to better preserve the temporal smoothness between consecutive slices of the tensor, the basis matrices are jointly learned by introducing an autoregressive model. An experiment on the commonly used Cambridge hand-gesture database demonstrates that our proposed method reaches convergence within a small number of iterations during the training stage and achieves promising results compared with state-of-the-art methods.