The goal of this work is to recover human body configurations from static images. Without assuming a priori knowledge of scale, pose or appearance, this problem is extremely challenging and demands the use of all possible sources of information. We develop a framework which can incorporate arbitrary pairwise constraints between body parts, such as scale compatibility, relative position, symmetry of clothing and smooth contour connections between parts. We detect candidate body parts from bottom-up using parallelism, and use various pairwise configuration constraints to assemble them together into body configurations. To find the most probable configuration, we solve an integer quadratic programming problem with a standard technique using linear approximations. Approximate IQP allows us to incorporate much more information than the traditional dynamic programming and remains computationally efficient. 15 hand-labeled images are used to train the low-level part detector and learn the pairwise constraints. We show test results on a variety of images.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Recovering human body configurations using pairwise constraints between parts


    Beteiligte:
    Xiaofeng Ren, (Autor:in) / Berg, A.C. (Autor:in) / Malik, J. (Autor:in)


    Erscheinungsdatum :

    2005-01-01


    Format / Umfang :

    532307 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Recovering Human Body Configurations Using Pairwise Constraints between Parts

    Ren, X. / Berg, A. / Malik, J. et al. | British Library Conference Proceedings | 2005


    Recovering Human Body Configurations: Combining Segmentation and Recognition

    Mori, G. / Ren, X. / Efros, A. et al. | British Library Conference Proceedings | 2004


    Recovering human body configurations: combining segmentation and recognition

    Mori, G. / Xiaofeng Ren, / Efros, A.A. et al. | IEEE | 2004


    Semi-supervised image database categorization using pairwise constraints

    Grira, N. / Crucianu, M. / Boujemaa, N. | IEEE | 2005