A better similarity mapping function across heterogeneous high-dimensional features is very desirable for many applications involving multi-modal data. In this paper, we introduce coupled dictionary learning (DL) into supervised sparse coding for multi-modal (cross-media) retrieval. We call this Supervised coupled dictionary learning with group structures for Multi-Modal retrieval (SliM2). SliM2 formulates the multi-modal mapping as a constrained dictionary learning problem. By utilizing the

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... rinsic power of DL to deal with the heterogeneous features, SliM2 extends unimodal DL to multi-modal DL. Moreover, the label information is employed in SliM2 to discover the shared structure inside intra-modality within the same class by a mixed norm (i.e., 'l1/l2'-norm). As a result, the multimodal retrieval is conducted via a set of jointly learned mapping functions across multi-modal data. The experimental results show the effectiveness of our proposed model when applied to cross-media retrieval.