Globally Variance-Constrained Sparse Representation and Its Application in Image Set Coding

Sparse representation leads to an efficient way to approximately recover a signal by the linear composition of a few bases from a learnt dictionary based on which various successful applications have been achieved. However, in the scenario of data compression, its efficiency and popularity are hinde...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	IEEE transactions on image processing Ročník 27; číslo 8; s. 3753 - 3765
Hlavní autori:	Zhang, Xiang, Sun, Jiarui, Ma, Siwei, Lin, Zhouchen, Zhang, Jian, Wang, Shiqi, Gao, Wen
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.08.2018
Predmet:	alternating direction method of multipliers Channel coding Dictionaries Entropy Image coding Machine learning Matching pursuit algorithms Sparse representation
ISSN:	1057-7149, 1941-0042, 1941-0042
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Abstract	Sparse representation leads to an efficient way to approximately recover a signal by the linear composition of a few bases from a learnt dictionary based on which various successful applications have been achieved. However, in the scenario of data compression, its efficiency and popularity are hindered. It is because of the fact that encoding sparsely distributed coefficients may consume more bits for representing the index of nonzero coefficients. Therefore, introducing an accurate rate constraint in sparse coding and dictionary learning becomes meaningful, which has not been fully exploited in the context of sparse representation. According to the Shannon entropy inequality, the variance of Gaussian distributed data bound its entropy, indicating the actual bitrate can be well estimated by its variance. Hence, a globally variance-constrained sparse representation (GVCSR) model is proposed in this paper, where a variance-constrained rate term is introduced to the optimization process. Specifically, we employ the alternating direction method of multipliers (ADMMs) to solve the non-convex optimization problem for sparse coding and dictionary learning, both of them have shown the state-of-the-art rate-distortion performance for image representation. Furthermore, we investigate the potential of applying the GVCSR algorithm in the practical image set compression, where the optimized dictionary is trained to efficiently represent the images captured in similar scenarios by implicitly utilizing inter-image correlations. Experimental results have demonstrated superior rate-distortion performance against the state-of-the-art methods.
AbstractList	Sparse representation leads to an efficient way to approximately recover a signal by the linear composition of a few bases from a learnt dictionary based on which various successful applications have been achieved. However, in the scenario of data compression, its efficiency and popularity are hindered. It is because of the fact that encoding sparsely distributed coefficients may consume more bits for representing the index of nonzero coefficients. Therefore, introducing an accurate rate constraint in sparse coding and dictionary learning becomes meaningful, which has not been fully exploited in the context of sparse representation. According to the Shannon entropy inequality, the variance of Gaussian distributed data bound its entropy, indicating the actual bitrate can be well estimated by its variance. Hence, a globally variance-constrained sparse representation (GVCSR) model is proposed in this paper, where a variance-constrained rate term is introduced to the optimization process. Specifically, we employ the alternating direction method of multipliers (ADMMs) to solve the non-convex optimization problem for sparse coding and dictionary learning, both of them have shown the state-of-the-art rate-distortion performance for image representation. Furthermore, we investigate the potential of applying the GVCSR algorithm in the practical image set compression, where the optimized dictionary is trained to efficiently represent the images captured in similar scenarios by implicitly utilizing inter-image correlations. Experimental results have demonstrated superior rate-distortion performance against the state-of-the-art methods. Sparse representation leads to an efficient way to approximately recover a signal by the linear composition of a few bases from a learnt dictionary based on which various successful applications have been achieved. However, in the scenario of data compression, its efficiency and popularity are hindered. It is because of the fact that encoding sparsely distributed coefficients may consume more bits for representing the index of nonzero coefficients. Therefore, introducing an accurate rate constraint in sparse coding and dictionary learning becomes meaningful, which has not been fully exploited in the context of sparse representation. According to the Shannon entropy inequality, the variance of Gaussian distributed data bound its entropy, indicating the actual bitrate can be well estimated by its variance. Hence, a globally variance-constrained sparse representation (GVCSR) model is proposed in this paper, where a variance-constrained rate term is introduced to the optimization process. Specifically, we employ the alternating direction method of multipliers (ADMMs) to solve the non-convex optimization problem for sparse coding and dictionary learning, both of them have shown the state-of-the-art rate-distortion performance for image representation. Furthermore, we investigate the potential of applying the GVCSR algorithm in the practical image set compression, where the optimized dictionary is trained to efficiently represent the images captured in similar scenarios by implicitly utilizing inter-image correlations. Experimental results have demonstrated superior rate-distortion performance against the state-of-the-art methods.Sparse representation leads to an efficient way to approximately recover a signal by the linear composition of a few bases from a learnt dictionary based on which various successful applications have been achieved. However, in the scenario of data compression, its efficiency and popularity are hindered. It is because of the fact that encoding sparsely distributed coefficients may consume more bits for representing the index of nonzero coefficients. Therefore, introducing an accurate rate constraint in sparse coding and dictionary learning becomes meaningful, which has not been fully exploited in the context of sparse representation. According to the Shannon entropy inequality, the variance of Gaussian distributed data bound its entropy, indicating the actual bitrate can be well estimated by its variance. Hence, a globally variance-constrained sparse representation (GVCSR) model is proposed in this paper, where a variance-constrained rate term is introduced to the optimization process. Specifically, we employ the alternating direction method of multipliers (ADMMs) to solve the non-convex optimization problem for sparse coding and dictionary learning, both of them have shown the state-of-the-art rate-distortion performance for image representation. Furthermore, we investigate the potential of applying the GVCSR algorithm in the practical image set compression, where the optimized dictionary is trained to efficiently represent the images captured in similar scenarios by implicitly utilizing inter-image correlations. Experimental results have demonstrated superior rate-distortion performance against the state-of-the-art methods.
Author	Wen Gao Jiarui Sun Jian Zhang Shiqi Wang Xiang Zhang Zhouchen Lin Siwei Ma
Author_xml	– sequence: 1 givenname: Xiang orcidid: 0000-0002-6002-1503 surname: Zhang fullname: Zhang, Xiang – sequence: 2 givenname: Jiarui surname: Sun fullname: Sun, Jiarui – sequence: 3 givenname: Siwei orcidid: 0000-0002-2731-5403 surname: Ma fullname: Ma, Siwei – sequence: 4 givenname: Zhouchen orcidid: 0000-0003-1493-7569 surname: Lin fullname: Lin, Zhouchen – sequence: 5 givenname: Jian surname: Zhang fullname: Zhang, Jian – sequence: 6 givenname: Shiqi surname: Wang fullname: Wang, Shiqi – sequence: 7 givenname: Wen surname: Gao fullname: Gao, Wen
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/29698207$$D View this record in MEDLINE/PubMed
BookMark	eNp9kE1v1DAQhi1URD_gjoSEfOSSZWwnjn2sVqWsVAlECxcO1sQZV0ZZJ9jZQ_89WWXLgQOnGY2e95XmuWRnaUzE2FsBGyHAfnzYfd1IEGYjjVRNrV-wC2FrUQHU8mzZoWmrVtT2nF2W8gtA1I3Qr9i5tNoaCe0F-3k7jB0OwxP_gTli8lRtx1TmjDFRz-8nzIX4N5oyFUozznFMHFPPd3Ph19M0RL_eYuK7PT4Sv6eZb8c-psfX7GXAodCb07xi3z_dPGw_V3dfbnfb67vKK2HnSjWdV16h9apDrZXAnjSoTlMHgbrQNuhb8K33Er0JCCHoEIyRCKY3Qqor9mHtnfL4-0BldvtYPA0DJhoPxUlQUllrjV3Q9yf00O2pd1OOe8xP7lnIAugV8HksJVNwPq5fH5UMToA7mneLeXc0707mlyD8E3zu_k_k3RqJRPQXN0oJY4X6Ax38jvM
CODEN	IIPRE4
CitedBy_id	crossref_primary_10_1109_TCYB_2019_2899005 crossref_primary_10_1109_TMM_2020_2967646 crossref_primary_10_1016_j_patrec_2019_10_026 crossref_primary_10_1109_ACCESS_2024_3368699 crossref_primary_10_3390_electronics8030303 crossref_primary_10_1109_TCC_2022_3218784 crossref_primary_10_1109_TCC_2021_3106103 crossref_primary_10_1016_j_jvcir_2020_102847
Cites_doi	10.1109/ICASSP.2015.7178168 10.1109/TIP.2006.881969 10.1016/0898-1221(76)90003-1 10.1109/ICMEW.2016.7574708 10.1109/ISCAS.2012.6271583 10.1109/TIP.2011.2181525 10.1137/140998135 10.1109/VCIP.2013.6706334 10.1109/ICASSP.2011.5946782 10.1109/ICCV.2009.5459452 10.1109/CVPR.2008.4587706 10.1109/TSP.2010.2040671 10.1109/JPROC.2010.2044470 10.1109/ICASSP.2014.6853576 10.1016/j.dsp.2006.02.002 10.1109/ICCV.2013.34 10.1561/2200000016 10.1109/DCC.2016.18 10.1109/ICIP.2001.958518 10.1109/TIP.2014.2323127 10.1109/TIP.2016.2629447 10.1109/LSP.2013.2258912 10.1109/ICIP.1998.723617 10.1109/ISCAS.2015.7168816 10.1109/ISCAS.2015.7168873 10.1016/j.sigpro.2013.09.025 10.1109/TCSVT.2014.2317886 10.1109/TIP.2016.2598483 10.1007/s11277-013-1577-y 10.1016/j.sigpro.2016.05.036 10.1016/j.jvcir.2008.03.001 10.1109/ICASSP.2014.6853954 10.1016/j.acha.2008.07.002 10.1109/ICCV.2011.6126377 10.1109/79.733497 10.1109/TCSVT.2012.2221191 10.1109/TIP.2007.903259 10.1109/TIP.2016.2623487 10.1109/TCSVT.2014.2302380 10.1109/PACRIM.2015.7334808 10.1007/s10915-010-9408-8 10.1111/j.2517-6161.1996.tb02080.x 10.1109/TIT.2009.2016006 10.1109/SMC.2015.276 10.1109/VCIP.2013.6706358 10.1109/TSP.2009.2036477 10.1109/JSTSP.2011.2135332 10.1109/ICASSP.1999.760624 10.1109/TIP.2013.2256917 10.1109/TSP.2006.881199 10.1109/ICME.2013.6607570 10.1109/JETCAS.2014.2298291 10.1038/381607a0 10.1109/TIT.2007.909108 10.1109/JSAC.2014.2328171 10.1109/MMSP.2012.6343419 10.1109/TCSVT.2015.2511838 10.1145/103085.103089 10.1109/TIP.2013.2287996 10.1109/TIP.2013.2266579 10.1007/978-1-4419-7011-4 10.1109/TIP.2015.2500034
ContentType	Journal Article
DBID	97E RIA RIE AAYXX CITATION NPM 7X8
DOI	10.1109/TIP.2018.2823546
DatabaseName	IEEE Xplore (IEEE) IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef PubMed MEDLINE - Academic
DatabaseTitle	CrossRef PubMed MEDLINE - Academic
DatabaseTitleList	PubMed MEDLINE - Academic
Database_xml	– sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: RIE name: IEEE Xplore url: https://ieeexplore.ieee.org/ sourceTypes: Publisher – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Applied Sciences Engineering
EISSN	1941-0042
EndPage	3765
ExternalDocumentID	29698207 10_1109_TIP_2018_2823546 8331891
Genre	orig-research Journal Article
GrantInformation_xml	– fundername: National Basic Research Program of China (973 Program) grantid: 2015CB352502 – fundername: Qualcomm funderid: 10.13039/100005144 – fundername: National Postdoctoral Program for Innovative Talents grantid: BX201600006 – fundername: National Natural Science Foundation of China grantid: 61625301; 61731018 funderid: 10.13039/501100001809 – fundername: Microsoft Research Asia funderid: 10.13039/100004318 – fundername: National Natural Science Foundation of China grantid: 61571017 funderid: 10.13039/501100001809
GroupedDBID	--- -~X .DC 0R~ 29I 4.4 53G 5GY 5VS 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABFSI ABQJQ ABVLG ACGFO ACGFS ACIWK AENEX AETIX AGQYO AGSQL AHBIQ AI. AIBXA AKJIK AKQYR ALLEH ALMA_UNASSIGNED_HOLDINGS ASUFR ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 E.L EBS EJD F5P HZ~ H~9 ICLAB IFIPE IFJZH IPLJI JAVBF LAI M43 MS~ O9- OCL P2P RIA RIE RNS TAE TN5 VH1 AAYXX CITATION AAYOK NPM PKN RIG Z5M 7X8
ID	FETCH-LOGICAL-c319t-35bc3c3a9c3ba6631ade603b6eb0febf75ac70c7cc2ac8fa0ff6ff882a08d8123
IEDL.DBID	RIE
ISICitedReferencesCount	10
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000430967600007&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1057-7149 1941-0042
IngestDate	Mon Sep 29 05:16:24 EDT 2025 Wed Feb 19 02:40:57 EST 2025 Sat Nov 29 03:21:08 EST 2025 Tue Nov 18 21:52:29 EST 2025 Wed Aug 27 02:49:00 EDT 2025
IsPeerReviewed	true
IsScholarly	true
Issue	8
Language	English
License	https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c319t-35bc3c3a9c3ba6631ade603b6eb0febf75ac70c7cc2ac8fa0ff6ff882a08d8123
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ORCID	0000-0003-1493-7569 0000-0002-2731-5403 0000-0002-6002-1503
PMID	29698207
PQID	2032399989
PQPubID	23479
PageCount	13
ParticipantIDs	crossref_citationtrail_10_1109_TIP_2018_2823546 crossref_primary_10_1109_TIP_2018_2823546 proquest_miscellaneous_2032399989 ieee_primary_8331891 pubmed_primary_29698207
PublicationCentury	2000
PublicationDate	2018-Aug. 2018-8-00 2018-Aug 20180801
PublicationDateYYYYMMDD	2018-08-01
PublicationDate_xml	– month: 08 year: 2018 text: 2018-Aug.
PublicationDecade	2010
PublicationPlace	United States
PublicationPlace_xml	– name: United States
PublicationTitle	IEEE transactions on image processing
PublicationTitleAbbrev	TIP
PublicationTitleAlternate	IEEE Trans Image Process
PublicationYear	2018
Publisher	IEEE
Publisher_xml	– name: IEEE
References	ref57 ref13 ref12 horev (ref41) 2012 ref15 ref58 ref14 ref53 ref52 ref55 ref11 ref54 ref10 zhang (ref19) 2012 lin (ref59) 2011 ref17 ref16 ref18 ref50 kim (ref77) 1997 ref46 ref45 ref48 ref47 ref42 ref44 ref43 ref49 ref8 ref7 ref9 ref4 ref5 ref40 ref35 ref37 ref36 ref75 ref31 ref74 ref33 ref76 ref32 salvatierra (ref38) 2010 ref1 ref39 kodak (ref61) 0 mairal (ref34) 2010; 11 zhang (ref30) 2017 olshausen (ref3) 1996; 381 ref71 ref70 larson (ref60) 0 ref73 feng (ref56) 2014 hyvärinen (ref6) 2009; 39 ref72 shannon (ref51) 1974 taubman (ref2) 2012; 642 ref68 ref24 ref67 ref23 ref26 ref69 ref25 ref64 ref20 ref63 ref66 ref65 ref21 ref27 ref29 liu (ref28) 2013 ref62 ryen (ref22) 2002; 3
References_xml	– ident: ref73 doi: 10.1109/ICASSP.2015.7178168 – ident: ref10 doi: 10.1109/TIP.2006.881969 – ident: ref54 doi: 10.1016/0898-1221(76)90003-1 – ident: ref23 doi: 10.1109/ICMEW.2016.7574708 – ident: ref11 doi: 10.1109/ISCAS.2012.6271583 – start-page: 674 year: 2012 ident: ref19 article-title: Image primitive coding and visual quality assessment publication-title: Proc Advances in Multimedia Information Processing – ident: ref24 doi: 10.1109/TIP.2011.2181525 – ident: ref58 doi: 10.1137/140998135 – ident: ref68 doi: 10.1109/VCIP.2013.6706334 – ident: ref49 doi: 10.1109/ICASSP.2011.5946782 – ident: ref26 doi: 10.1109/ICCV.2009.5459452 – ident: ref74 doi: 10.1109/CVPR.2008.4587706 – ident: ref35 doi: 10.1109/TSP.2010.2040671 – ident: ref5 doi: 10.1109/JPROC.2010.2044470 – ident: ref17 doi: 10.1109/ICASSP.2014.6853576 – ident: ref32 doi: 10.1016/j.dsp.2006.02.002 – ident: ref65 doi: 10.1109/ICCV.2013.34 – volume: 642 year: 2012 ident: ref2 publication-title: JPEG2000 Image Compression Fun-damentals Standards and Practice Image Compression Fundamentals Standards and Practice – ident: ref29 doi: 10.1561/2200000016 – ident: ref66 doi: 10.1109/DCC.2016.18 – ident: ref21 doi: 10.1109/ICIP.2001.958518 – ident: ref12 doi: 10.1109/TIP.2014.2323127 – year: 1974 ident: ref51 publication-title: A Mathematical Theory of Communication – ident: ref67 doi: 10.1109/TIP.2016.2629447 – ident: ref36 doi: 10.1109/LSP.2013.2258912 – ident: ref20 doi: 10.1109/ICIP.1998.723617 – ident: ref8 doi: 10.1109/ISCAS.2015.7168816 – ident: ref71 doi: 10.1109/ISCAS.2015.7168873 – volume: 39 year: 2009 ident: ref6 publication-title: Natural Image Statistics A Probabilistic Approach to Early Computational Vision – ident: ref13 doi: 10.1016/j.sigpro.2013.09.025 – ident: ref25 doi: 10.1109/TCSVT.2014.2317886 – ident: ref48 doi: 10.1109/TIP.2016.2598483 – start-page: 592 year: 2012 ident: ref41 article-title: Adaptive image compression using sparse dictionaries publication-title: Proc IEEE Int Conf Syst Signals Image Process (IWSSIP) – ident: ref42 doi: 10.1007/s11277-013-1577-y – ident: ref40 doi: 10.1016/j.sigpro.2016.05.036 – ident: ref43 doi: 10.1016/j.jvcir.2008.03.001 – ident: ref50 doi: 10.1109/ICASSP.2014.6853954 – ident: ref63 doi: 10.1016/j.acha.2008.07.002 – ident: ref27 doi: 10.1109/ICCV.2011.6126377 – ident: ref18 doi: 10.1109/79.733497 – ident: ref76 doi: 10.1109/TCSVT.2012.2221191 – ident: ref44 doi: 10.1109/TIP.2007.903259 – ident: ref53 doi: 10.1109/TIP.2016.2623487 – ident: ref14 doi: 10.1109/TCSVT.2014.2302380 – ident: ref72 doi: 10.1109/PACRIM.2015.7334808 – year: 1997 ident: ref77 article-title: Very low bit-rate embedded video coding with 3D set partitioning in hierarchical trees (3D SPIHT) – year: 2010 ident: ref38 publication-title: New Sparse Representation Methods Application to Image Compression and Indexing – ident: ref55 doi: 10.1007/s10915-010-9408-8 – ident: ref37 doi: 10.1111/j.2517-6161.1996.tb02080.x – ident: ref64 doi: 10.1109/TIT.2009.2016006 – year: 0 ident: ref60 publication-title: Categorical Image Quality (CSIQ) Database – ident: ref16 doi: 10.1109/SMC.2015.276 – ident: ref7 doi: 10.1109/VCIP.2013.6706358 – ident: ref39 doi: 10.1109/TSP.2009.2036477 – ident: ref45 doi: 10.1109/JSTSP.2011.2135332 – volume: 3 start-page: iii-2177 year: 2002 ident: ref22 article-title: A rate-distortion optimal coding alternative to matching pursuit publication-title: Proc IEEE Int Conf Acoust Speech Signal Process (ICASSP) – ident: ref31 doi: 10.1109/ICASSP.1999.760624 – ident: ref52 doi: 10.1109/TIP.2013.2256917 – ident: ref33 doi: 10.1109/TSP.2006.881199 – ident: ref69 doi: 10.1109/ICME.2013.6607570 – ident: ref70 doi: 10.1109/JETCAS.2014.2298291 – volume: 381 start-page: 607 year: 1996 ident: ref3 article-title: Emergence of simple-cell receptive field properties by learning a sparse code for natural images publication-title: Nature doi: 10.1038/381607a0 – ident: ref62 doi: 10.1109/TIT.2007.909108 – year: 0 ident: ref61 publication-title: Kodak lossless true color image suite (PhotoCD PCD0992) – ident: ref57 doi: 10.1109/JSAC.2014.2328171 – ident: ref46 doi: 10.1109/MMSP.2012.6343419 – ident: ref9 doi: 10.1109/TCSVT.2015.2511838 – start-page: 93 year: 2013 ident: ref28 article-title: Image super-resolution via hierarchical and collaborative sparse representation publication-title: Proc Data Compress Conf (DCC) – ident: ref1 doi: 10.1145/103085.103089 – ident: ref47 doi: 10.1109/TIP.2013.2287996 – start-page: 380 year: 2017 ident: ref30 article-title: Globally variance-constrained sparse representation for rate-distortion optimized image representation publication-title: Proc Data Compress Conf (DCC) – year: 2014 ident: ref56 publication-title: An alternating direction method approach to cloud traffic management – volume: 11 start-page: 19 year: 2010 ident: ref34 article-title: Online learning for matrix factorization and sparse coding publication-title: J Mach Learn Res – start-page: 612 year: 2011 ident: ref59 article-title: Linearized alternating direction method with adaptive penalty for low-rank representation publication-title: Proc Adv Neural Inf Process Syst – ident: ref15 doi: 10.1109/TIP.2013.2266579 – ident: ref4 doi: 10.1007/978-1-4419-7011-4 – ident: ref75 doi: 10.1109/TIP.2015.2500034
SSID	ssj0014516
Score	2.3350854
Snippet	Sparse representation leads to an efficient way to approximately recover a signal by the linear composition of a few bases from a learnt dictionary based on...
SourceID	proquest pubmed crossref ieee
SourceType	Aggregation Database Index Database Enrichment Source Publisher
StartPage	3753
SubjectTerms	alternating direction method of multipliers Channel coding Dictionaries Entropy Image coding Machine learning Matching pursuit algorithms Sparse representation
Title	Globally Variance-Constrained Sparse Representation and Its Application in Image Set Coding
URI	https://ieeexplore.ieee.org/document/8331891 https://www.ncbi.nlm.nih.gov/pubmed/29698207 https://www.proquest.com/docview/2032399989
Volume	27
WOSCitedRecordID	wos000430967600007&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVIEE databaseName: IEEE Xplore customDbUrl: eissn: 1941-0042 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0014516 issn: 1057-7149 databaseCode: RIE dateStart: 19920101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LSwMxEB5UPOjBR33VFxG8CKZdm-5mc5Si2IsUXxQ8LNk0gULdirsV_PfOZLerBxW87SHZR77JzjeZF8CpSJF26q7gqEw172rnuEY9y0PUDRHyfZdG2jebkLe38XCoBgtwXufCWGt98Jlt0aX35Y-mZkZHZe1YoARSqvqilFGZq1V7DKjhrPdshpJLpP1zl2Sg2g_9AcVwxS00L0RIVPebCvI9VX6nl17NXK__7wU3YK2ik-yyxH8TFmzWgPWKWrJq4-YNWP1Wd3ALnstK_5MP9oSmMuHOqXGnbxdB017R2rXszgfJVrlJGdPZiPWLnF1-ubzZOGP9F_wjsXtbsN6U9OA2PF5fPfRueNVlgRvcfgUXYWqEEVoZkWrkHxd6ZKNApJFNA2dTJ0NtZGCkMR1tYqcD5yLnkJjrIB4hPRA7sJRNM7sHzIZKOytRLtDOUd0Al6Ibh2lHiIjyd0UT2vOFT0xVgpw-bZJ4UyRQCUKVEFRJBVUTzuoZr2X5jT_GbhEi9bgKjCaczLFNcOuQP0RndjrLE2oej_xMxaoJuyXo9eSOihSSI7n_800PYIUeXUYCHsJS8TazR7Bs3otx_naM8jmMj718fgKBkeDt
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LS8QwEB5EBfXg-nZ9RvAiGLduto8cRRQXdRFdRfBQ0mwCgnZltyv4751Js9WDCt56SErab9L5pjOTD2BfZEg7VUtwdKaKt5S1XKGf5SH6hgj5vs0i5cQm4k4neXyUNxNwWPXCGGNc8Zk5okuXy-_19Yh-lTUSgRZIrepTpJzlu7WqnAFJzrrcZhjzGIn_OCkZyEa3fUNVXMkRBhgiJLL7zQk5VZXfCaZzNOe1_y1xAeY9oWQnpQUswoTJl6DmySXzW3e4BHPfTh5chqfyrP-XD_aAwTIhz0m60wlG0LQ3jHcNu3Vlsr47KWcq77F2MWQnX0lv9pyz9it-k9idKdhpnzzhCtyfn3VPL7jXWeAaN2DBRZhpoYWSWmQKGcix6pkoEFlkssCazMah0nGgY62bSidWBdZG1iI1V0HSQ4IgVmEy7-dmHZgJpbImRsvASEe2AnwVrSTMmkJE1MEr6tAYv_hU-0PI6dFeUheMBDJFqFKCKvVQ1eGgmvFWHsDxx9hlQqQa58Gow94Y2xQ3D2VEVG76o2FK8vHI0GQi67BWgl5NbspIIj2KN36-6S7MXHSvr9KrdudyE2ZpGWVd4BZMFoOR2YZp_V48Dwc7zko_ARDC404
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Globally+Variance-Constrained+Sparse+Representation+and+Its+Application+in+Image+Set+Coding&rft.jtitle=IEEE+transactions+on+image+processing&rft.au=Zhang%2C+Xiang&rft.au=Sun%2C+Jiarui&rft.au=Ma%2C+Siwei&rft.au=Lin%2C+Zhouchen&rft.date=2018-08-01&rft.issn=1057-7149&rft.eissn=1941-0042&rft.volume=27&rft.issue=8&rft.spage=3753&rft.epage=3765&rft_id=info:doi/10.1109%2FTIP.2018.2823546&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_TIP_2018_2823546
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1057-7149&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1057-7149&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1057-7149&client=summon