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Multimodal Similarity-Preserving Hashing

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4 Author(s)
Masci, J. ; Swiss AI Lab. (IDSIA), Univ. of Lugano (USI), Lugano, Switzerland ; Bronstein, M.M. ; Bronstein, A.M. ; Schmidhuber, J.

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 complex forms. We show experimentally that our method significantly outperforms state-of-the-art hashing approaches on multimedia retrieval tasks.

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Published in:

Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:36 ,  Issue: 4 )

Date of Publication:

April 2014

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