CASIA OpenIR  > 智能感知与计算研究中心
Supervised Discrete Hashing With Relaxation
Gui, Jie1,2; Liu, Tongliang3,4; Sun, Zhenan5; Tao, Dacheng6; Tan, Tieniu5
Source PublicationIEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
2018-03-01
Volume29Issue:3Pages:608-617
SubtypeArticle
AbstractData-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.
KeywordData-dependent Hashing Least Squares Regression Supervised Discrete Hashing (Sdh) Supervised Discrete Hashing With Relaxation (Sdhr)
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TNNLS.2016.2636870
WOS KeywordLEARNING BINARY-CODES ; ITERATIVE QUANTIZATION ; PROCRUSTEAN APPROACH ; IMAGE RETRIEVAL ; RECOGNITION ; SCENE
Indexed BySCI
Language英语
Funding OrganizationNational Science Foundation of China(61572463 ; Open Project Program of the National Laboratory of Pattern Recognition (NLPR)(201700027) ; CCF-Tencent Open Fund ; Australian Research Council(DP-140102164 ; 61573360) ; FT-130101457 ; LE-140100061)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000426344600009
Citation statistics
Cited Times:11[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20763
Collection智能感知与计算研究中心
Corresponding AuthorSun, Zhenan
Affiliation1.Chinese Acad Sci, Inst Intelligent Machines, Hefei 230031, Anhui, Peoples R China
2.Shenzhen Univ, Shenzhen Key Lab Media Secur, Shenzhen 518060, Peoples R China
3.Univ Technol Sydney, Ctr Artificial Intelligence, Sydney, NSW 2007, Australia
4.Univ Technol Sydney, Fac Engn & Informat Technol, Sydney, NSW 2007, Australia
5.Chinese Acad Sci, Ctr Res Intelligent Percept & Comp, Natl Lab Pattern Recognit, Inst Automat,CAS Ctr Excellence Brain Sci & Intel, Beijing 100190, Peoples R China
6.Univ Sydney, Sch Informat Technol, Fac Engn & Informat Technol, Sydney, NSW 2006, Australia
Recommended Citation
GB/T 7714
Gui, Jie,Liu, Tongliang,Sun, Zhenan,et al. Supervised Discrete Hashing With Relaxation[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2018,29(3):608-617.
APA Gui, Jie,Liu, Tongliang,Sun, Zhenan,Tao, Dacheng,&Tan, Tieniu.(2018).Supervised Discrete Hashing With Relaxation.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,29(3),608-617.
MLA Gui, Jie,et al."Supervised Discrete Hashing With Relaxation".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 29.3(2018):608-617.
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