Supervised Discrete Hashing With Relaxation

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

Supervised Discrete Hashing With Relaxation

Gui, J Liu, T

Sun, Z Tao, D

Tan, T

Permalink

Publication Type:: Journal Article
Citation:: IEEE Transactions on Neural Networks and Learning Systems, 2018, 29 (3), pp. 608 - 617
Issue Date:: 2018-03-01

Closed Access

	Filename	Description	Size
	07801881.pdf	Published Version	2.73 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Gui, J	en_US
dc.contributor.author	Liu, T https://orcid.org/0000-0002-9640-6472	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	2018-03-01	en_US
dc.identifier.citation	IEEE Transactions on Neural Networks and Learning Systems, 2018, 29 (3), pp. 608 - 617	en_US
dc.identifier.issn	2162-237X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/134273
dc.description.abstract	© 2016 IEEE. Data-dependent hashing has recently attracted attention due to being able to support efficient retrieval and storage of high-dimensional data, such as documents, images, and videos. In this paper, we propose a novel learning-based hashing method called "supervised discrete hashing with relaxation" (SDHR) based on "supervised discrete hashing" (SDH). SDH uses ordinary least squares regression and traditional zero-one matrix encoding of class label information as the regression target (code words), thus fixing the regression target. In SDHR, the regression target is instead optimized. The optimized regression target matrix satisfies a large margin constraint for correct classification of each example. Compared with SDH, which uses the traditional zero-one matrix, SDHR utilizes the learned regression target matrix and, therefore, more accurately measures the classification error of the regression model and is more flexible. As expected, SDHR generally outperforms SDH. Experimental results on two large-scale image data sets (CIFAR-10 and MNIST) and a large-scale and challenging face data set (FRGC) demonstrate the effectiveness and efficiency of SDHR.	en_US
dc.relation.ispartof	IEEE Transactions on Neural Networks and Learning Systems	en_US
dc.relation.isbasedon	10.1109/TNNLS.2016.2636870	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Supervised Discrete Hashing With Relaxation	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	29	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	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	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	29	en_US

Abstract:

© 2016 IEEE. Data-dependent hashing has recently attracted attention due to being able to support efficient retrieval and storage of high-dimensional data, such as documents, images, and videos. In this paper, we propose a novel learning-based hashing method called "supervised discrete hashing with relaxation" (SDHR) based on "supervised discrete hashing" (SDH). SDH uses ordinary least squares regression and traditional zero-one matrix encoding of class label information as the regression target (code words), thus fixing the regression target. In SDHR, the regression target is instead optimized. The optimized regression target matrix satisfies a large margin constraint for correct classification of each example. Compared with SDH, which uses the traditional zero-one matrix, SDHR utilizes the learned regression target matrix and, therefore, more accurately measures the classification error of the regression model and is more flexible. As expected, SDHR generally outperforms SDH. Experimental results on two large-scale image data sets (CIFAR-10 and MNIST) and a large-scale and challenging face data set (FRGC) demonstrate the effectiveness and efficiency of SDHR.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/134273