Improved subspace clustering via exploitation of spatial constraints
Pham, Duc-Son, Saha, Budhaditya, Phung, Dinh and Venkatesh, Svetha 2012, Improved subspace clustering via exploitation of spatial constraints, in CVPR 2012 : Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, Piscataway, N. J., pp. 550-557.
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Title
Improved subspace clustering via exploitation of spatial constraints
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.
ISBN
1467312266 9781467312264
ISSN
1063-6919
Language
eng
Field of Research
080104 Computer Vision
Socio Economic Objective
970108 Expanding Knowledge in the Information and Computing Sciences
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