Abstract
Action 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.
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Azary, S., Savakis, A. (2013). Grassmannian Spectral Regression for Action Recognition. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2013. Lecture Notes in Computer Science, vol 8034. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41939-3_19
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DOI: https://doi.org/10.1007/978-3-642-41939-3_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41938-6
Online ISBN: 978-3-642-41939-3
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