Multimodal Similarity-Preserving Hashing

Abstract

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.

DOI: 10.1109/TPAMI.2013.225

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@article{Masci2014MultimodalSH, title={Multimodal Similarity-Preserving Hashing}, author={Jonathan Masci and Michael M. Bronstein and Alexander M. Bronstein and J{\"{u}rgen Schmidhuber}, journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, year={2014}, volume={36}, pages={824-830} }