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With the advantage of low storage cost and high retrieval efficiency, hashingtechniques have recently been an emerging topic in cross-modal similaritysearch. As multiple modal data reflect similar semantic content, manyresearches aim at learning unified binary codes. However, discriminativehashing features learned by these methods are not adequate. This results inlower accuracy and robustness. We propose a novel hashing learning frameworkwhich jointly performs classifier learning, subspace learning and matrixfactorization to preserve class-specific semantic content, termedDiscriminative Supervised Hashing (DSH), to learn the discrimative unifiedbinary codes for multi-modal data. Besides, reducing the loss of informationand preserving the non-linear structure of data, DSH non-linearly projectsdifferent modalities into the common space in which the similarity amongheterogeneous data points can be measured. Extensive experiments conducted onthe three publicly available datasets demonstrate that the framework proposedin this paper outperforms several state-of -the-art methods.

Discriminative Supervised Hashing for Cross-Modal similarity Search