Spectral embedded hashing for scalable image retrieval

Chen, L; Xu, D; Tsang, IWH; Li, X

Spectral embedded hashing for scalable image retrieval

Chen, L Xu, D

Tsang, IWH

Li, X

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Cybernetics, 2014, 44 (7), pp. 1180 - 1190
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Chen, L	en_US
dc.contributor.author	Xu, D https://orcid.org/0000-0003-2775-9730	en_US
dc.contributor.author	Tsang, IWH https://orcid.org/0000-0001-8095-4637	en_US
dc.contributor.author	Li, X	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	IEEE Transactions on Cybernetics, 2014, 44 (7), pp. 1180 - 1190	en_US
dc.identifier.issn	2168-2267	en_US
dc.identifier.uri	http://hdl.handle.net/10453/118149
dc.description.abstract	We propose a new graph based hashing method called spectral embedded hashing (SEH) for large-scale image retrieval. We first introduce a new regularizer into the objective function of the recent work spectral hashing to control the mismatch between the resultant hamming embedding and the low-dimensional data representation, which is obtained by using a linear regression function. This linear regression function can be employed to effectively handle the out-of-sample data, and the introduction of the new regularizer makes SEH better cope with the data sampled from a nonlinear manifold. Considering that SEH cannot efficiently cope with the high dimensional data, we further extend SEH to kernel SEH (KSEH) to improve the efficiency and effectiveness, in which a nonlinear regression function can also be employed to obtain the low dimensional data representation. We also develop a new method to efficiently solve the approximate solution for the eigenvalue decomposition problem in SEH and KSEH. Moreover, we show that some existing hashing methods are special cases of our KSEH. Our comprehensive experiments on CIFAR, Tiny-580K, NUS-WIDE, and Caltech-256 datasets clearly demonstrate the effectiveness of our methods. © 2013 IEEE.	en_US
dc.relation.ispartof	IEEE Transactions on Cybernetics	en_US
dc.relation.isbasedon	10.1109/TCYB.2013.2281366	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.subject.mesh	Animals	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Linear Models	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Image Processing, Computer-Assisted	en_US
dc.subject.mesh	Databases, Factual	en_US
dc.title	Spectral embedded hashing for scalable image retrieval	en_US
dc.type	Journal Article
utslib.citation.volume	7	en_US
utslib.citation.volume	44	en_US
utslib.for	080101 Adaptive Agents and Intelligent Robotics	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	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
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	7	en_US
pubs.publication-status	Published	en_US
pubs.volume	44	en_US

Abstract:

We propose a new graph based hashing method called spectral embedded hashing (SEH) for large-scale image retrieval. We first introduce a new regularizer into the objective function of the recent work spectral hashing to control the mismatch between the resultant hamming embedding and the low-dimensional data representation, which is obtained by using a linear regression function. This linear regression function can be employed to effectively handle the out-of-sample data, and the introduction of the new regularizer makes SEH better cope with the data sampled from a nonlinear manifold. Considering that SEH cannot efficiently cope with the high dimensional data, we further extend SEH to kernel SEH (KSEH) to improve the efficiency and effectiveness, in which a nonlinear regression function can also be employed to obtain the low dimensional data representation. We also develop a new method to efficiently solve the approximate solution for the eigenvalue decomposition problem in SEH and KSEH. Moreover, we show that some existing hashing methods are special cases of our KSEH. Our comprehensive experiments on CIFAR, Tiny-580K, NUS-WIDE, and Caltech-256 datasets clearly demonstrate the effectiveness of our methods. © 2013 IEEE.

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http://hdl.handle.net/10453/118149