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Multimodal Similarity-Preserving Hashing
2014
IEEE Transactions on Pattern Analysis and Machine Intelligence
We introduce an efficient computational framework for hashing data belonging to multiple modalities into a single representation space where they become mutually comparable. The proposed approach is based on a novel coupled siamese neural network architecture and allows unified treatment of intra-and inter-modality similarity learning. Unlike existing cross-modality similarity learning approaches, our hashing functions are not limited to binarized linear projections and can assume arbitrarily
doi:10.1109/tpami.2013.225
pmid:26353203
fatcat:biaqx2ztzjeyhi2n35ry7updra