Grassmannian Spectral Regression for Action Recognition

Abstract

AbstractAction recognition from multiple views and computational performance associated with high-dimensional data are common challenges for real-world action classification systems. Subspace learning has received considerable attention as a means of finding an efficient low-dimensional representation that leads to better classification and efficient processing. In this paper we propose Grassmannian Spectral Regression (GRASP), a novel subspace learning algorithm which combines the benefits of Grassmann manifolds and spectral regression for fast and accurate classification. A Grassmann manifold is a space that promotes smooth surfaces where points represent subspaces and the relationship between points is defined by a mapping of an orthogonal matrix. Spectral regression is a regularized subspace learning approach that overcomes the disadvantages of eigen-based approaches. We demonstrate the effectiveness of GRASP on computationally intensive, multi-view action classification using the INRIA IXMAS dataset and the i3DPost Multi-View dataset.KeywordsLinear Discriminant AnalysisAction RecognitionAction ClassificationLocality Preserve ProjectionHuman Action RecognitionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.