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ICCV
2009
IEEE

Spectral clustering of linear subspaces for motion segmentation

10 years 5 months ago
Spectral clustering of linear subspaces for motion segmentation
This paper studies automatic segmentation of multiple motions from tracked feature points through spectral embedding and clustering of linear subspaces. We show that the dimension of the ambient space is crucial for separability, and that low dimensions chosen in prior work are not optimal. We suggest lower and upper bounds together with a data-driven procedure for choosing the optimal ambient dimension. Application of our approach to the Hopkins155 video benchmark database uniformly outperforms a range of state-of-the-art methods both in terms of segmentation accuracy and computational speed
Fabien Lauer, Christoph Schn¨orr
Added 13 Jul 2009
Updated 10 Jan 2010
Type Conference
Year 2009
Where ICCV
Authors Fabien Lauer, Christoph Schn¨orr
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