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Collective Bayesian Matrix factorization Hashing for cross-modal retrieval

Matrix factorization hashing approaches have been widely applied in large scale cross-modality visual search due to their efficiency to preserve similarities among multimodal features. In this paper, we propose a novel cross-modality hashing technic based on Bayesian matrix factorization that factorizes all modalities into a shared latent semantic space using the Bayesian inference. To achieve better search performance, we measure the similarity using the cosine distance. Several experiments prove that the proposed method achieves better performance than many known methods on cross-modal retrieval applications.