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In this paper we address the problem of generative object categorization in computer vision. We propose a Bayesian model using hierarchical Dirichlet processes mixing AdaBoost learning. Although previous methods trained HDP model for one or two latent themes, our proposed approach uses small-patch-independent-words of appearance-based descriptor and shape information to train a set of intermediate...
We consider the problem of visual categorization with minimal supervision during training. We propose a partbased model that loosely captures structural information. We represent images as a collection of parts characterized by an appearance codeword from a visual vocabulary and by a neighborhood context, organized in an ordered set of bag-of-features representations. These bags are computed in a...
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