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2010
Springer

Visualization and clustering of crowd video content in MPCA subspace

8 years 8 months ago
Visualization and clustering of crowd video content in MPCA subspace
This paper presents a novel approach for the visualization and clustering of crowd video contents by using multilinear principal component analysis (MPCA). In contrast to feature-point-based approach and frame-based dimensionality reduction approach, the proposed method maps each short video segment to a point in MPCA subspace to take temporal information into account naturally through tensorial representations. Specifically, MPCA projects each short segment of a video to a low-dimensional tensor first. A few MPCA features are then selected according to the variance captured as the final representation. Thus, a video is visualized as a trajectory in MPCA subspace. The trajectory generated enables visual interpretation of video content in a compact space as well as visual clustering of video events. The proposed method is evaluated on the PETS 2009 datasets through comparison with three existing methods for video visualization. The MPCA visualization shows superior performance in cl...
Haiping Lu, How-Lung Eng, Myo Thida, Konstantinos
Added 24 Jan 2011
Updated 24 Jan 2011
Type Journal
Year 2010
Where CIKM
Authors Haiping Lu, How-Lung Eng, Myo Thida, Konstantinos N. Plataniotis
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