CROSS-MEDIA RETRIEVAL WITH SEMANTICS CLUSTERING AND ENHANCEMENT

Cross-media retrieval, which uses a text query to search for images and vice-versa, has attracted a wide attention in recent years. The mostly existing cross-media retrieval methods aim at finding a common subspace and maximizing different modalities correlations. But these approaches do not directly capture the underlying semantic information of different modalities. This paper proposes a novel cross-media retrieval by semantics clustering and enhancement, where a semantic-preserved mapping is learned from the original space to the target semantic space. Meanwhile, In order to improve the demarcation of semantic space, we enhance the semantic manifold by learning a dimension invariant matrix. Our approach not only maximizes the correlation between different modalities, but also increases the discriminative ability among different categories. Experiments show that our approach outperforms the popular methods on two real word datasets.