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
Author(s) Pham, Duc-Son
Saha, Budhaditya
Phung, Dinh
Venkatesh, Svetha
Conference name Computer Vision and Pattern Recognition. Conference (2012 : Providence, R. I.)
Conference location Providence, R. I.
Conference dates 16-21 Jun. 2012
Title of proceedings CVPR 2012 : Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Editor(s) [Unknown]
Publication date 2012
Conference series Computer Vision and Pattern Recognition. Conference (2012 : Providence, R. I.)
Start page 550
End page 557
Total pages 8
Publisher IEEE
Place of publication Piscataway, N. J.
Keyword(s) computational costs
corrupted data
data sets
Hopkins
motion segmentation
numerical algorithms
sparse representation
sparse solutions
spatial constraints
spatial geometry
subspace clustering
Summary 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 9781467312264
9781467312271
ISSN 1063-6919
Language eng
Field of Research 080104 Computer Vision
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
Persistent URL http://hdl.handle.net/10536/DRO/DU:30049552

Document type: Conference Paper
Collection: Centre for Pattern Recognition and Data Analytics
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