Hierarchical Gaussianization for Image Classification

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 we extract the appearance information from the GMM parameters, and the spatial information from global and local statistics over Gaussian maps. Finally, we employ a supervised dimension reduction technique called DAP (discriminant attribute projection) to remove noise directions and to further enhance the discriminating power of our representation. We justify that the traditional histogram representation and the spatial pyramid matching are special cases of our hierarchical Gaussianization. We compare our new representation with other approaches in scene classification, object recognition and face recognition, and our performance ranks among the top in all three tasks.