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    View-based clustering of object appearances based on independent subspace analysis

    View-based clustering of object appearances based on independent subspace analysis


    In 3D object detection and recognition, an object of interest is subject to changes in view as well as in illumination and shape. For image classification purpose, it is desirable to derive a representation in which intrinsic characteristics of the object are captured in a low dimensional space while effects due to artifacts are reduced. In this paper, we propose a method for view-based unsupervised learning of object appearances. First, view-subspaces are learned from a view-unlabeled data set of multi-view appearances, using independent subspace analysis (ISA). A learned view-subspace provides a representation of appearances at that view, regardless of illumination effect. A measure, called view-subspace activity, is calculated thereby to provide a metric for view-based classification. View-based clustering is then performed by using maximum view-subspace activity (MVSA) criterion. This work is to the best of our knowledge the first devoted research on view-based clustering of images.

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    Titel :

    View-based clustering of object appearances based on independent subspace analysis

    Beteiligte:
    Li, S.Z. (Autor:in) / Xiao Guang Lv, (Autor:in) / Hong Jiang Zhang, (Autor:in)
    Erschienen in:

    Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 ; 2 ; 295-300 vol.2

    Erscheinungsdatum :

    2001-01-01

    Format / Umfang :

    889908 byte

    ISBN :

    0-7695-1143-0

    DOI :

    https://doi.org/10.1109/ICCV.2001.937639

    Medientyp :

    Aufsatz (Konferenz)

    Format :

    Elektronische Ressource

    Sprache :

    Englisch

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