This paper presents a novel framework that can combine latent semantic learning and reduced hypergraph learning for semi-supervised image categorization. To improve the traditional bag-of-features representation, we first propose a semantics-aware representation which can learn latent semantics automatically from a large vocabulary of abundant visual keywords through contextual spectral embedding. The learnt latent semantics can be readily used to define a histogram intersection kernel. Based on this semantics-aware kernel, we further develop a reduced hypergraph-based semi-supervised learning method to exploit both labeled and unlabeled images for image categorization. Experimental results have shown that the proposed framework can achieve significant improvements with respect to the state of the arts.
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