Minimal Basis Facility Location for Subspace Segmentation
Abstract
In contrast to the current motion segmentation paradigm that assumes independence between the motion subspaces, we approach the motion segmentation problem by seeking the parsimonious basis set that can represent the data. Our formulation explicitly looks for the overlap between subspaces in order to achieve a minimal basis representation. This parsimonious basis set is important for the performance of our model selection scheme because the sharing of basis results in savings of model complexity cost. We propose the use of affinity propagation based method to determine the number of motion. The key lies in the incorporation of a global cost model into the factor graph, serving
Cite
Text
Lee and Cheong. "Minimal Basis Facility Location for Subspace Segmentation." International Conference on Computer Vision, 2013. doi:10.1109/ICCV.2013.200Markdown
[Lee and Cheong. "Minimal Basis Facility Location for Subspace Segmentation." International Conference on Computer Vision, 2013.](https://mlanthology.org/iccv/2013/lee2013iccv-minimal/) doi:10.1109/ICCV.2013.200BibTeX
@inproceedings{lee2013iccv-minimal,
title = {{Minimal Basis Facility Location for Subspace Segmentation}},
author = {Lee, Choon-Meng and Cheong, Loong-Fah},
booktitle = {International Conference on Computer Vision},
year = {2013},
doi = {10.1109/ICCV.2013.200},
url = {https://mlanthology.org/iccv/2013/lee2013iccv-minimal/}
}