We present a method for automatically learning discriminative image patches for the recognition of given object classes. The approach applies discriminative training of log-linear models to image patch histograms. We show that it works well on three tasks and performs significantly better than other methods using the same features. For example, the method decides that patches containing an eye are most important for distinguishing face from background images. The recognition performance is very competitive with error rates presented in other publications. In particular, a new best error rate for the Caltech motorbikes data of 1.5% is achieved.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Discriminative training for object recognition using image patches


    Beteiligte:
    Deselaers, T. (Autor:in) / Keysers, D. (Autor:in) / Ney, H. (Autor:in)


    Erscheinungsdatum :

    01.01.2005


    Format / Umfang :

    514287 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Object recognition using discriminative parts

    Liu, Y. H. / Lee, A. J. / Chang, F. | British Library Online Contents | 2012


    Learning Discriminative Canonical Correlations for Object Recognition with Image Sets

    Kim, T.-K. / Kittler, J. / Cipolla, R. | British Library Conference Proceedings | 2006




    Discriminative Training for HMM-Based Offline Handwritten Character Recognition

    Nopsuwanchai, R. / Povey, D. / Institute of Electrical and Electronics Engineers | British Library Conference Proceedings | 2003