Conference Paper

Action Classification on Product Manifolds

DOI: 10.1109/CVPR.2010.5540131 Conference: The Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2010, San Francisco, CA, USA, 13-18 June 2010
Source: DBLP

ABSTRACT Videos can be naturally represented as multidimensional arrays known as tensors. However, the geometry of the tensor space is often ignored. In this paper, we argue that the underlying geometry of the tensor space is an important property for action classification. We characterize a tensor as a point on a product manifold and perform classification on this space. First, we factorize a tensor relating to each order using a modified High Order Singular Value Decomposition (HOSVD). We recognize each factorized space as a Grassmann manifold. Consequently, a tensor is mapped to a point on a product manifold and the geodesic distance on a product manifold is computed for tensor classification. We assess the proposed method using two public video databases, namely Cambridge-Gesture gesture and KTH human action data sets. Experimental results reveal that the proposed method performs very well on these data sets. In addition, our method is generic in the sense that no prior training is needed.

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    • "Method Set1 Set2 Set3 Set4 Overall TCCA [16] 0.81 0.81 0.78 0.86 0.82 PM [14] 0.89 0.86 0.89 0.87 0.88 TB [15] 0.93 0.88 0.90 0.91 0.91 Cov3D [13] 0.92 0.94 0.94 0.93 0.93 Our method 0.92 0.93 0.97 0.95 0.94 (a) Dislike gesture. (b) Point gesture. "
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