Normalized Cuts has successfully been applied to a wide range of tasks in computer vision, it is indisputably one of the most popular segmentation algorithms in use today. A number of extensions to this approach have also been proposed, ones that can deal with multiple classes or that can incorporate a priori information in the form of grouping constraints. It was recently shown how a general linearly constrained Normalized Cut problem can be solved. This was done by proving that strong duality holds for the Lagrangian relaxation of such problems. This provides a principled way to perform multi-class partitioning while enforcing any linear constraints exactly.

    The Lagrangian relaxation requires the maximization of the algebraically smallest eigenvalue over a one-dimensional matrix sub-space. This is an unconstrained, piece-wise differentiable and concave problem. In this paper we show how to solve this optimization efficiently even for very large-scale problems. The method has been tested on real data with convincing results.


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    Titel :

    Efficiently Solving the Fractional Trust Region Problem


    Beteiligte:
    Yagi, Yasushi (Herausgeber:in) / Kang, Sing Bing (Herausgeber:in) / Kweon, In So (Herausgeber:in) / Zha, Hongbin (Herausgeber:in) / Eriksson, Anders P. (Autor:in) / Olsson, Carl (Autor:in) / Kahl, Fredrik (Autor:in)

    Kongress:

    Asian Conference on Computer Vision ; 2007 ; Tokyo, Japan November 18, 2007 - November 22, 2007


    Erschienen in:

    Computer Vision – ACCV 2007 ; Kapitel : 78 ; 796-805


    Erscheinungsdatum :

    01.01.2007


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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