Tensor Canonical Correlation Analysis for Action Classification

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.

• Type: Conference
• Year: 2007
• Where: CVPR
• Authors: Tae-Kyun Kim, Shu-Fai Wong, Roberto Cipolla
• Keywords: Action Detection Method, Canonical Correlation Analysis, Classical Canonical Correlation, Computer Vision, CVPR 2007, Tensor Canonical Correlation, Tensor Cca