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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A new framework for approximate labeling via graph cuts


    Contributors:


    Publication date :

    2005-01-01


    Size :

    296090 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    A New Framework for Approximate Labeling via Graph Cuts

    Komodakis, N. / Tziritas, G. / IEEE | British Library Conference Proceedings | 2005


    A new primal-dual algorithm for multilabel graph-cuts problems with approximate moves

    Cheng, Ziang / Liu, Yang / Liu, Guojun | British Library Online Contents | 2017


    A new primal-dual algorithm for multilabel graph-cuts problems with approximate moves

    Cheng, Ziang / Liu, Yang / Liu, Guojun | British Library Online Contents | 2017



    Illumination-invariant tracking via graph cuts

    Freedman, D. / Turek, M.W. | IEEE | 2005