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Efficient structured support vector regression

Conference Paper


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Abstract


  • Support Vector Regression (SVR) has been a long standing

    problem in machine learning, and gains its popularity on various

    computer vision tasks. In this paper, we propose a structured support

    vector regression framework by extending the max-margin principle to

    incorporate spatial correlations among neighboring pixels. The objective

    function in our framework considers both label information and pairwise

    features, helping to achieve better cross-smoothing over neighboring

    nodes. With the bundle method, we effectively reduce the number

    of constraints and alleviate the adverse effect of outliers, leading to an

    efficient and robust learning algorithm. Moreover, we conduct a thorough

    analysis for the loss function used in structured regression, and

    provide a principled approach for defining proper loss functions and deriving

    the corresponding solvers to find the most violated constraint. We

    demonstrate that our method outperforms the state-of-the-art regression

    approaches on various testbeds of synthetic images and real-world scenes.

Authors


  •   Jia, Ke (external author)
  •   Wang, Lei
  •   Liu, Nianjun (external author)

Publication Date


  • 2010

Citation


  • Jia, K., Wang, L. & Liu, N. (2010). Efficient structured support vector regression. 10th Asian Conference on Computer Vision (ACCV) (pp. 1-13). Berlin Heidelberg: Springer-Verlag.

Scopus Eid


  • 2-s2.0-79952524854

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1535&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/529

Has Global Citation Frequency


Start Page


  • 1

End Page


  • 13

Place Of Publication


  • Berlin Heidelberg

Abstract


  • Support Vector Regression (SVR) has been a long standing

    problem in machine learning, and gains its popularity on various

    computer vision tasks. In this paper, we propose a structured support

    vector regression framework by extending the max-margin principle to

    incorporate spatial correlations among neighboring pixels. The objective

    function in our framework considers both label information and pairwise

    features, helping to achieve better cross-smoothing over neighboring

    nodes. With the bundle method, we effectively reduce the number

    of constraints and alleviate the adverse effect of outliers, leading to an

    efficient and robust learning algorithm. Moreover, we conduct a thorough

    analysis for the loss function used in structured regression, and

    provide a principled approach for defining proper loss functions and deriving

    the corresponding solvers to find the most violated constraint. We

    demonstrate that our method outperforms the state-of-the-art regression

    approaches on various testbeds of synthetic images and real-world scenes.

Authors


  •   Jia, Ke (external author)
  •   Wang, Lei
  •   Liu, Nianjun (external author)

Publication Date


  • 2010

Citation


  • Jia, K., Wang, L. & Liu, N. (2010). Efficient structured support vector regression. 10th Asian Conference on Computer Vision (ACCV) (pp. 1-13). Berlin Heidelberg: Springer-Verlag.

Scopus Eid


  • 2-s2.0-79952524854

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1535&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/529

Has Global Citation Frequency


Start Page


  • 1

End Page


  • 13

Place Of Publication


  • Berlin Heidelberg