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    Improved Subspace Clustering via Exploitation of Spatial Constraints

    Access Status
    Fulltext not available
    Authors
    Pham, DucSon
    Budhaditya, Saha
    Phung, Dinh
    Venkatesh, Svetha
    Date
    2012
    Type
    Conference Paper
    
    Metadata
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    Citation
    Pham, Duc-Son and Budhaditya, Saha and Phung, Dinh and Venkatesh, Svetha. 2012. Improved Subspace Clustering via Exploitation of Spatial Constraints, in IEEE Conference on Computer Vision and Pattern Recognition, Jun 16-21 2012, pp. 550-557. Providence, Rhode Island: IEEE.
    Source Title
    The IEEE International Conference on Computer Vision and Pattern Recognition
    Source Conference
    The IEEE International Conference on Computer Vision and Pattern Recognition
    DOI
    10.1109/CVPR.2012.6247720
    ISSN
    1063-6919
    URI
    http://hdl.handle.net/20.500.11937/4473
    Collection
    • Curtin Research Publications
    Abstract

    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.

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