Improved subspace clustering via exploitation of spatial constraints

Duc Son Pham, Saha Budhaditya, Dinh Phung, Svetha Venkatesh

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34 Citations (Scopus)


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.

Original languageEnglish
Title of host publication2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012
Number of pages8
Publication statusPublished - 1 Oct 2012
Externally publishedYes
EventIEEE Conference on Computer Vision and Pattern Recognition 2012 - Providence, United States of America
Duration: 16 Jun 201221 Jun 2012 (Proceedings)

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919


ConferenceIEEE Conference on Computer Vision and Pattern Recognition 2012
Abbreviated titleCVPR 2012
CountryUnited States of America
Internet address

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