Recent work [1], has shown that improving model learning for weak classifiers can yield significant gains in the overall accuracy of a boosted classifier. However, most published classifier boosting research relies only on rudimentary learning techniques for weak classifiers. So while it is known that improving the model learning can greatly improve the accuracy of the resulting strong classifier, it remains to be shown how much can yet be gained by further improving the model learning at the weak classifier level. This paper derives a very accurate model learning method for weak classifiers based on the popular Haar-like features and presents an investigation of its usefulness compared to the standard and recent approaches. The accuracy of the new method is shown by demonstrating the new models ability to predict ROC performance on validation data. A discussion of the problems in learning accurate weak hypotheses is given, along with example solutions. It is also shown that a previous simpler method can be further improved. Lastly, we show that improving model accuracy does not continue to yield improved overall classification beyond a certain point. At this point the learning technique, in this case RealBoost, is unable to make gains from the improved model data. The method has been tested on pedestrian detection tasks using classifiers boosted using the RealBoost boosting algorithm. A subset of our most interesting results is shown to demonstrate the value of method.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    On the importance of accurate weak classifier learning for boosted weak classifiers


    Beteiligte:
    Overett, Gary (Autor:in) / Petersson, Lars (Autor:in)


    Erscheinungsdatum :

    01.06.2008


    Format / Umfang :

    406717 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    On the Importance of Accurate Weak Classifier Learning for Boosted Weak Classifiers

    Overett, G.M. / Petersson, L. | British Library Conference Proceedings | 2008


    Response Binning: Improved Weak Classifiers for Boosting

    Rasolzadeh, B. / Petersson, L. / Pettersson, N. | British Library Conference Proceedings | 2006


    Response Binning: Improved Weak Classifiers for Boosting

    Rasolzadeh, B. / Petersson, L. / Pettersson, N. | IEEE | 2006


    Application of a fuzzy integral for weak classifiers boosting

    Samorodov, A. V. | British Library Online Contents | 2011


    The histogram feature - a resource-efficient Weak Classifier

    Pettersson, Niklas / Petersson, Lars / Andersson, Lars | IEEE | 2008