Sparse representation was originally used in signal processing as a powerful tool for acquiring, representing and compressing high-dimensional signals. Recently, motivated by the great successes it has achieved, it has become a hot research topic in the domain of computer vision and pattern recognition. In this paper, we propose to adapt sparse representation to the problem of Visual Object Categorization which aims at predicting whether at least one or several objects of some given categories are present in an image. Thus, we have elaborated a reconstructive and discriminative sparse representation of images, which integrates a discriminative term, such as Fisher discriminative measure or the output of a SVM classifier, into the standard sparse representation objective function in order to learn a reconstructive and discriminative dictionary. Experiments carried out on the SIMPLIcity image dataset have clearly revealed that our reconstructive and discriminative approach has gained an obvious improvement of the classification accuracy compared to standard SVM using image features as input. Moreover, the results have shown that our approach is more efficient than a sparse representation being only reconstructive, which indicates that adding a discriminative term for constructing the sparse representation is more suitable for the categorization purpose.