The improvement of bag-of-features image representation by statistical modeling of visual tokens has recently gained attention in the field of object categorization. This paper proposes a soft bag-of-features image representation based on Gaussian Mixture Modeling (GMM) of visual tokens for object categorization. The distribution of local features from each visual token is assumed as the GMM and learned from the training data by the Expectation-Maximization algorithm with a model selection method based on the Minimum Description Length. Consequently, we can employ Bayesian formula to compute posterior probabilities of being visual tokens for local features. According to these probabilities, three schemes of image representation are defined and compared for object categorization under a new discriminative learning framework of Bayesian classifiers, the Max-Min posterior Pseudo-probabilities (MMP). We evaluate the effectiveness of the proposed object categorization approach on the Caltech-4 database and car side images from the University of Illinois. The experimental results with comparisons to those reported in other related work show that our approach is promising.