In this paper, we propose a novel image representation for scene classification. Firstly, we model multiple order statistics of image patches via Gaussian Mixture Model(GMM) in a Bayesian framework. Secondly, we combine the information of mean and covariance of the GMM and represent it as a mean-covariance supervector through a new distance metric. Experimental results demonstrate that our new representation, by just using nearest centroid classifier, has significantly outperformed all existing methods on the fifteen scene category database.