A new framework is presented that uses tools from duality theory of linear programming to derive graph-cut based combinatorial algorithms for approximating NP-hard classification problems. The derived algorithms include /spl alpha/-expansion graph cut techniques merely as a special case, have guaranteed optimality properties even in cases where /spl alpha/-expansion techniques fail to do so and can provide very tight per-instance suboptimality bounds in all occasions.


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    Titel :

    A new framework for approximate labeling via graph cuts


    Beteiligte:
    Komodakis, N. (Autor:in) / Tziritas, G. (Autor:in)


    Erscheinungsdatum :

    2005-01-01


    Format / Umfang :

    296090 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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