Multimodal similarity-preserving hashing

Jonathan Masci; Michael M. Bronstein; Alexander M. Bronstein; Jurgen Schmidhuber

doi:10.1109/TPAMI.2013.225

Multimodal similarity-preserving hashing

Jonathan Masci, Michael M. Bronstein, Alexander M. Bronstein, Jurgen Schmidhuber

Research output: Contribution to journal › Article › peer-review

153 Scopus citations

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. © 2014 IEEE.

Original language	English (US)
Pages (from-to)	824-830
Number of pages	7
Journal	IEEE Transactions on Pattern Analysis and Machine Intelligence
Volume	36
Issue number	4
DOIs	https://doi.org/10.1109/TPAMI.2013.225
State	Published - Jan 1 2014
Externally published	Yes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2022-09-14

ASJC Scopus subject areas

Artificial Intelligence
Computational Theory and Mathematics
Software
Applied Mathematics
Computer Vision and Pattern Recognition

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10.1109/TPAMI.2013.225

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AU - Bronstein, Michael M.

AU - Bronstein, Alexander M.

AU - Schmidhuber, Jurgen

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PY - 2014/1/1

Y1 - 2014/1/1

N2 - 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. © 2014 IEEE.

AB - 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. © 2014 IEEE.

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JO - IEEE Transactions on Pattern Analysis and Machine Intelligence

JF - IEEE Transactions on Pattern Analysis and Machine Intelligence

IS - 4

ER -

Multimodal similarity-preserving hashing

Abstract

Bibliographical note

ASJC Scopus subject areas

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