Fast Supervised Discrete Hashing

Gui, J; Liu, T; Sun, Z; Tao, D; Tan, T

Fast Supervised Discrete Hashing

Gui, J Liu, T Sun, Z Tao, D

Tan, T

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40 (2), pp. 490 - 496
Issue Date:: 2018-02-01

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Field	Value	Language
dc.contributor.author	Gui, J	en_US
dc.contributor.author	Liu, T	en_US
dc.contributor.author	Sun, Z	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Tan, T	en_US
dc.date.issued	2018-02-01	en_US
dc.identifier.citation	IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40 (2), pp. 490 - 496	en_US
dc.identifier.issn	0162-8828	en_US
dc.identifier.uri	http://hdl.handle.net/10453/131886
dc.description.abstract	© 2017 IEEE. Learning-based hashing algorithms are 'hot topics' because they can greatly increase the scale at which existing methods operate. In this paper, we propose a new learning-based hashing method called 'fast supervised discrete hashing' (FSDH) based on 'supervised discrete hashing' (SDH). Regressing the training examples (or hash code) to the corresponding class labels is widely used in ordinary least squares regression. Rather than adopting this method, FSDH uses a very simple yet effective regression of the class labels of training examples to the corresponding hash code to accelerate the algorithm. To the best of our knowledge, this strategy has not previously been used for hashing. Traditional SDH decomposes the optimization into three sub-problems, with the most critical sub-problem - discrete optimization for binary hash codes - solved using iterative discrete cyclic coordinate descent (DCC), which is time-consuming. However, FSDH has a closed-form solution and only requires a single rather than iterative hash code-solving step, which is highly efficient. Furthermore, FSDH is usually faster than SDH for solving the projection matrix for least squares regression, making FSDH generally faster than SDH. For example, our results show that FSDH is about 12-times faster than SDH when the number of hashing bits is 128 on the CIFAR-10 data base, and FSDH is about 151-times faster than FastHash when the number of hashing bits is 64 on the MNIST data-base. Our experimental results show that FSDH is not only fast, but also outperforms other comparative methods.	en_US
dc.relation.ispartof	IEEE Transactions on Pattern Analysis and Machine Intelligence	en_US
dc.relation.isbasedon	10.1109/TPAMI.2017.2678475	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Fast Supervised Discrete Hashing	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	40	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	40	en_US

Abstract:

© 2017 IEEE. Learning-based hashing algorithms are 'hot topics' because they can greatly increase the scale at which existing methods operate. In this paper, we propose a new learning-based hashing method called 'fast supervised discrete hashing' (FSDH) based on 'supervised discrete hashing' (SDH). Regressing the training examples (or hash code) to the corresponding class labels is widely used in ordinary least squares regression. Rather than adopting this method, FSDH uses a very simple yet effective regression of the class labels of training examples to the corresponding hash code to accelerate the algorithm. To the best of our knowledge, this strategy has not previously been used for hashing. Traditional SDH decomposes the optimization into three sub-problems, with the most critical sub-problem - discrete optimization for binary hash codes - solved using iterative discrete cyclic coordinate descent (DCC), which is time-consuming. However, FSDH has a closed-form solution and only requires a single rather than iterative hash code-solving step, which is highly efficient. Furthermore, FSDH is usually faster than SDH for solving the projection matrix for least squares regression, making FSDH generally faster than SDH. For example, our results show that FSDH is about 12-times faster than SDH when the number of hashing bits is 128 on the CIFAR-10 data base, and FSDH is about 151-times faster than FastHash when the number of hashing bits is 64 on the MNIST data-base. Our experimental results show that FSDH is not only fast, but also outperforms other comparative methods.

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http://hdl.handle.net/10453/131886