Incremental multi-linear discriminant analysis using canonical correlations for action recognition

Authors:
Cheng-Cheng Jia;Su-Jing Wang;Xu-Jun Peng;Wei Pang;Can-Yan Zhang;Chun-Guang Zhou;Zhe-Zhou Yu
Affiliations:
College of Computer Science and Technology, Jilin University, Changchun 130012, China;College of Computer Science and Technology, Jilin University, Changchun 130012, China;Raytheon BBN technologies, Boston, MA 02138, USA;College of Computer Science and Technology, Jilin University, Changchun 130012, China and School of Natural and Computing Sciences, University of Aberdeen, Aberdeen AB24 3UE, UK;College of Computer Science and Technology, Harbin Engineering University, Harbin 150000, China;College of Computer Science and Technology, Jilin University, Changchun 130012, China;College of Computer Science and Technology, Jilin University, Changchun 130012, China
Venue:
Neurocomputing
Year:
2012

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Abstract

Canonical correlations analysis (CCA) is often used for feature extraction and dimensionality reduction. However, the image vectorization of CCA breaks the spatial structure of the original image, and the excessive dimensions of vectors often cause the curse of dimensionality problem. In this paper, we propose a novel feature extraction method based on CCA in multi-linear discriminant subspace by encoding each action sample as a high-order tensor. An optimization approach is presented to iteratively learn the discriminant subspace by unfolding the tensor along different tensor modes, which shows that most of the underlying data structure, including the spatio-temporal information, is retained and the curse of dimensionality problem is alleviated by the use of the proposed approach. At the same time, an incremental scheme is developed for multi-linear subspace online learning, which can improve the discriminative capability efficiently and effectively. In addition, the nearest neighbor classifier (NNC) is employed for action classification. Experiments on the Weizmann database show that the proposed method outperforms the state-of-the-art methods in terms of accuracy and time complexity, and it is robust against partial occlusion.