We show that efficient object recognition can be obtained by combining informative features with linear classification. The results demonstrate the superiority of informative class-specific features, as compared with generic type features such as wavelets, for the task of object recognition. We show that information rich features can reach optimal performance with simple linear separation rules, while generic feature based classifiers require more complex classification schemes. This is significant because efficient and optimal methods have been developed for spaces that allow linear separation. To compare different strategies for feature extraction, we trained and compared classifiers working in feature spaces of the same low dimensionality, using two feature types (image fragments vs. wavelets) and two classification rules (linear hyperplane and a Bayesian network). The results show that by maximizing the individual information of the features, it is possible to obtain efficient classification by a simple linear separating rule, as well as more efficient learning.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Object recognition with informative features and linear classification


    Contributors:
    Vidal-Naquet, (author) / Ullman, (author)


    Publication date :

    2003-01-01


    Size :

    4926067 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Object Recognition with Informative Features and Linear Classification

    Vidal-Naquet, M. / Ullman, S. / IEEE | British Library Conference Proceedings | 2003


    Linear classifiers and selection of informative features

    Zhuravlev, Y. I. / Laptin, Y. P. / Vinogradov, A. P. et al. | British Library Online Contents | 2017



    Learning to Locate Informative Features for Visual Identification

    Ferencz, A. / Learned-Miller, E. G. / Malik, J. | British Library Online Contents | 2008


    Object recognition with adaptive Gabor features

    Alterson, R. / Spetsakis, M. | British Library Online Contents | 2004