We present a novel approach to improving subspace clustering by exploiting the spatial constraints. The new method encourages the sparse solution to be consistent with the spatial geometry of the tracked points, by embedding weights into the sparse formulation. By doing so, we are able to correct sparse representations in a principled manner without introducing much additional computational cost. We discuss alternative ways to treat the missing and corrupted data using the latest theory in robust lasso regression and suggest numerical algorithms so solve the proposed formulation. The experiments on the benchmark Johns Hopkins 155 dataset demonstrate that exploiting spatial constraints significantly improves motion segmentation.
History
Event
Computer Vision and Pattern Recognition. Conference (2012 : Providence, R. I.)
Pagination
550 - 557
Publisher
IEEE
Location
Providence, R. I.
Place of publication
Piscataway, N. J.
Start date
2012-06-16
End date
2012-06-21
ISSN
1063-6919
ISBN-13
9781467312264
ISBN-10
1467312266
Language
eng
Publication classification
E1 Full written paper - refereed
Title of proceedings
CVPR 2012 : Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition