Tensor Canonical Correlation Analysis for Action Classification

Abstract

We introduce a new framework, namely tensor canonical correlation analysis (TCCA) which is an extension of classical canonical correlation analysis (CCA) to multidimensional data arrays (or tensors) and apply this for action/gesture classification in videos. By tensor CCA, joint space-time linear relationships of two video volumes are inspected to yield flexible and descriptive similarity features of the two videos. The TCCA features are combined with a discriminative feature selection scheme and a nearest neighbor classifier for action classification. In addition, we propose a time-efficient action detection method based on dynamic learning of subspaces for tensor CCA for the case that actions are not aligned in the space-time domain. The proposed method delivered significantly better accuracy and comparable detection speed over state-of-the-art methods on the KTH action data set as well as self-recorded hand gesture data sets.