Visual dictionary learning as a crucial task of image representation has gained increasing attention. Specifically,
sparse coding is widely used due to its intrinsic advantage. In this paper, we propose a novel heterogeneous
latent semantic sparse coding model. The central idea is to bridge heterogeneous modalities by capturing their
common sparse latent semantic structure so that the learned visual dictionary is able to describe both the
visual and textual properties of training data. Experiments on both image categorization and retrieval tasks
demonstrate that our model shows superior performance over several recent methods such as K-means and Sparse
Coding.
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