ML Anthology

Spectral Clustering of Linear Subspaces for Motion Segmentation

Fabien Lauer, Christoph Schnörr
ICCV 2009 pp. 678-685
doi:10.1109/ICCV.2009.5459173 /iccv/2009/lauer2009iccv-spectral/

Abstract

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.

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Text

Lauer and Schnörr. "Spectral Clustering of Linear Subspaces for Motion Segmentation." IEEE/CVF International Conference on Computer Vision, 2009. doi:10.1109/ICCV.2009.5459173

Markdown

[Lauer and Schnörr. "Spectral Clustering of Linear Subspaces for Motion Segmentation." IEEE/CVF International Conference on Computer Vision, 2009.](https://mlanthology.org/iccv/2009/lauer2009iccv-spectral/) doi:10.1109/ICCV.2009.5459173

BibTeX

@inproceedings{lauer2009iccv-spectral,
  title     = {{Spectral Clustering of Linear Subspaces for Motion Segmentation}},
  author    = {Lauer, Fabien and Schnörr, Christoph},
  booktitle = {IEEE/CVF International Conference on Computer Vision},
  year      = {2009},
  pages     = {678-685},
  doi       = {10.1109/ICCV.2009.5459173},
  url       = {https://mlanthology.org/iccv/2009/lauer2009iccv-spectral/}
}