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In this paper, we propose a new image representation to capture both the appearance and spatial information for image classification applications. First, we model the feature vectors, from the whole corpus, from each image and at each individual patch, in a Bayesian hierarchical framework using mixtures of Gaussians. After such a hierarchical Gaussianization, each image is represented by a Gaussian mixture model (GMM) for its appearance, and several Gaussian maps for its spatial layout. Then wedoi:10.1109/iccv.2009.5459435 dblp:conf/iccv/ZhouCLLH09 fatcat:oqf2dg3s2vakfcrmpkyrlsmylq