Encoding Spatial Context for Large-Scale Partial-Duplicate Web Image Retrieval
Many recent state-of-the-art image retrieval approaches are based on Bag-of-Visual-Words model and represent an image with a set of visual words by quantizing local SIFT（scale invariant feature transform） features. Feature quantization reduces the discriminative power of local features and unavoidab...
Saved in:
| Published in | Journal of computer science and technology Vol. 29; no. 5; pp. 837 - 848 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
Boston
Springer US
01.09.2014
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1000-9000 1860-4749 |
| DOI | 10.1007/s11390-014-1472-3 |
Cover
| Abstract | Many recent state-of-the-art image retrieval approaches are based on Bag-of-Visual-Words model and represent an image with a set of visual words by quantizing local SIFT（scale invariant feature transform） features. Feature quantization reduces the discriminative power of local features and unavoidably causes many false local matches between images, which degrades the retrieval accuracy. To filter those false matches, geometric context among visual words has been popularly explored for the verification of geometric consistency. However, existing studies with global or local geometric verification are either computationally expensive or achieve limited accuracy. To address this issue, in this paper, we focus on partialduplicate Web image retrieval, and propose a scheme to encode the spatial context for visual matching verification. An efficient affine enhancement scheme is proposed to refine the verification results. Experiments on partial-duplicate Web image search, using a database of one million images, demonstrate the effectiveness and efficiency of the proposed approach.Evaluation on a 10-million image database further reveals the scalability of our approach. |
|---|---|
| AbstractList | Many recent state-of-the-art image retrieval approaches are based on Bag-of-Visual-Words model and represent an image with a set of visual words by quantizing local SIFT (scale invariant feature transform) features. Feature quantization reduces the discriminative power of local features and unavoidably causes many false local matches between images, which degrades the retrieval accuracy. To filter those false matches, geometric context among visual words has been popularly explored for the verification of geometric consistency. However, existing studies with global or local geometric verification are either computationally expensive or achieve limited accuracy. To address this issue, in this paper, we focus on partial duplicate Web image retrieval, and propose a scheme to encode the spatial context for visual matching verification. An efficient affine enhancement scheme is proposed to refine the verification results. Experiments on partial-duplicate Web image search, using a database of one million images, demonstrate the effectiveness and efficiency of the proposed approach. Evaluation on a 10-million image database further reveals the scalability of our approach. Many recent state-of-the-art image retrieval approaches are based on Bag-of-Visual-Words model and represent an image with a set of visual words by quantizing local SIFT（scale invariant feature transform） features. Feature quantization reduces the discriminative power of local features and unavoidably causes many false local matches between images, which degrades the retrieval accuracy. To filter those false matches, geometric context among visual words has been popularly explored for the verification of geometric consistency. However, existing studies with global or local geometric verification are either computationally expensive or achieve limited accuracy. To address this issue, in this paper, we focus on partialduplicate Web image retrieval, and propose a scheme to encode the spatial context for visual matching verification. An efficient affine enhancement scheme is proposed to refine the verification results. Experiments on partial-duplicate Web image search, using a database of one million images, demonstrate the effectiveness and efficiency of the proposed approach.Evaluation on a 10-million image database further reveals the scalability of our approach. |
| Author | 周文罡 李厚强 卢亦娟 田奇 |
| AuthorAffiliation | Chinese Academy of Sciences Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China, Hefei 230027, China Department of Electronic Engineering and Information Science, University of Science and Technology of China Hefei 230027, China Department of Computer Science, Texas State University, San Marcos, TX 78666, U.S.A. Department of Computer Science, University of Texas at San Antonio,San Antonio, TX 78249, U.S.A. |
| Author_xml | – sequence: 1 fullname: 周文罡 李厚强 卢亦娟 田奇 |
| BookMark | eNp9kUtv1DAUhS1UJPrgB7CLYMMC0-v4FS_RtEClEVQtiKXlOHZIyNhTOwPDv8ejVEXqohvbkr9z7tU5J-goxOAQekXgPQGQ55kQqgADYZgwWWP6DB2TRgBmkqmj8gYArMrxAp3kPAJQCYwdoy-XwcZuCH11uzXzYKZqFcPs9nPlY6rWJvUO31ozuerapMM_vthtp8Ga2VU_XFtdbUzvqhs3p8H9NtMZeu7NlN3L-_sUff94-W31Ga-_frpafVhjywidMWkYsdQo5TpnOchOtMTW3lsQHrxoLOe8AdFIMA03XHqoreJdbVgrWis7eoreLb5_TPAm9HqMuxTKRD3m8dd-zPtWu7qkARwIFPztgm9TvNu5POvNkK2bJhNc3GVNRK2oUJQ1BX3zCH2wrhVpuBTFtFByoWyKOSfntR3mkl_JLplh0gT0oRa91KLLIvpQi6ZFSR4pt2nYmPT3SU29aHJhQ-_S_52eEr2-H_Qzhv6u6B4mCVELaCSn9B8n96rA |
| CitedBy_id | crossref_primary_10_1145_2818708 crossref_primary_10_1109_TIP_2015_2389624 crossref_primary_10_1117_1_JEI_27_5_053002 crossref_primary_10_1007_s11390_015_1536_z |
| Cites_doi | 10.1109/TIP.2013.2251650 10.1109/CVPR.2012.6247910 10.1145/1873951.1874019 10.1109/ICCV.2003.1238663 10.1109/CVPR.2012.6248018 10.1109/CVPR.2006.102 10.1109/CVPR.2007.383222 10.1109/TIP.2014.2305072 10.1109/CVPR.2009.5206791 10.1109/TMM.2011.2167224 10.1007/978-3-540-88682-2_24 10.1109/CVPR.2006.68 10.1109/CVPR.2009.5206566 10.1109/ICCV.2013.210 10.1109/CVPR.2006.264 10.1109/TMM.2013.2270455 10.1145/2422956.2422960 10.1145/2393347.2393377 10.1109/CVPR.2011.5995601 10.1016/j.cviu.2013.12.011 10.1109/CVPR.2013.213 10.1109/TPAMI.1987.4767923 10.1109/CVPR.2008.4587635 10.1016/j.imavis.2004.02.006 10.1109/ICCV.2007.4408891 10.1109/34.993558 10.1109/CVPRW.2009.5206531 10.1145/1282280.1282359 10.1109/CVPR.2009.5206848 10.1109/TMM.2014.2305909 10.1109/CVPR.2007.383172 10.1109/CVPR.2007.382970 10.1109/TMM.2011.2165053 10.1109/CVPR.2005.221 10.1145/244130.244151 10.1145/358669.358692 10.5244/C.22.50 10.1109/CVPR.2010.5540039 10.1023/B:VISI.0000029664.99615.94 10.1109/TMM.2014.2301979 10.1109/CVPR.2012.6248038 10.1109/CVPR.2011.5995528 |
| ContentType | Journal Article |
| Copyright | Springer Science+Business Media New York 2014 Springer Science+Business Media New York 2014. Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
| Copyright_xml | – notice: Springer Science+Business Media New York 2014 – notice: Springer Science+Business Media New York 2014. – notice: Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
| DBID | 2RA 92L CQIGP W92 ~WA AAYXX CITATION 3V. 7SC 7WY 7WZ 7XB 87Z 8AL 8FD 8FE 8FG 8FK 8FL ABJCF ABUWG AFKRA ARAPS AZQEC BENPR BEZIV BGLVJ CCPQU DWQXO FRNLG F~G GNUQQ HCIFZ JQ2 K60 K6~ K7- L.- L6V L7M L~C L~D M0C M0N M7S P5Z P62 PHGZM PHGZT PKEHL PQBIZ PQBZA PQEST PQGLB PQQKQ PQUKI PRINS PTHSS Q9U 2B. 4A8 92I 93N PSX TCJ |
| DOI | 10.1007/s11390-014-1472-3 |
| DatabaseName | 维普期刊资源整合服务平台 中文科技期刊数据库-CALIS站点 维普中文期刊数据库 中文科技期刊数据库-工程技术 中文科技期刊数据库- 镜像站点 CrossRef ProQuest Central (Corporate) Computer and Information Systems Abstracts ABI/INFORM Collection (Proquest) ABI/INFORM Global (PDF only) ProQuest Central (purchase pre-March 2016) ABI/INFORM Collection Computing Database (Alumni Edition) Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central (Alumni) (purchase pre-March 2016) ABI/INFORM Collection (Alumni) ProQuest Materials Science & Engineering ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Central Business Premium Collection Technology Collection ProQuest One Community College ProQuest Central Korea Business Premium Collection (Alumni) ABI/INFORM Global (Corporate) ProQuest Central Student SciTech Premium Collection ProQuest Computer Science Collection ProQuest Business Collection (Alumni Edition) ProQuest Business Collection Computer Science Database ABI/INFORM Professional Advanced ProQuest Engineering Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional ABI/INFORM Global Computing Database Engineering Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic (New) ProQuest One Academic Middle East (New) ProQuest One Business (UW System Shared) ProQuest One Business (Alumni) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China Engineering Collection ProQuest Central Basic Wanfang Data Journals - Hong Kong WANFANG Data Centre Wanfang Data Journals 万方数据期刊 - 香港版 China Online Journals (COJ) China Online Journals (COJ) |
| DatabaseTitle | CrossRef ABI/INFORM Global (Corporate) ProQuest Business Collection (Alumni Edition) ProQuest One Business Computer Science Database ProQuest Central Student Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Central China ABI/INFORM Complete ProQuest Central ABI/INFORM Professional Advanced ProQuest One Applied & Life Sciences ProQuest Engineering Collection ProQuest Central Korea ProQuest Central (New) Advanced Technologies Database with Aerospace ABI/INFORM Complete (Alumni Edition) Engineering Collection Advanced Technologies & Aerospace Collection Business Premium Collection ABI/INFORM Global ProQuest Computing Engineering Database ABI/INFORM Global (Alumni Edition) ProQuest Central Basic ProQuest Computing (Alumni Edition) ProQuest One Academic Eastern Edition ProQuest Technology Collection ProQuest SciTech Collection ProQuest Business Collection Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database ProQuest One Academic UKI Edition Materials Science & Engineering Collection ProQuest One Business (Alumni) ProQuest One Academic ProQuest Central (Alumni) ProQuest One Academic (New) Business Premium Collection (Alumni) |
| DatabaseTitleList | Computer and Information Systems Abstracts ABI/INFORM Global (Corporate) |
| Database_xml | – sequence: 1 dbid: 8FG name: ProQuest Technology Collection url: https://search.proquest.com/technologycollection1 sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| DocumentTitleAlternate | Encoding Spatial Context for Large-Scale Partial-Duplicate Web Image Retrieval |
| EISSN | 1860-4749 |
| EndPage | 848 |
| ExternalDocumentID | jsjkxjsxb_e201405010 10_1007_s11390_014_1472_3 662608753 |
| GrantInformation_xml | – fundername: This work was supported in part to Dr. Wen-Gang Zhou by the Fundamental Research Funds for the Central Universities of China under Grant Nos. WK2100060014 and WK2100060011, the Start-Up Funding from the University of Science and Technology of China under Grant No. KY2100000036, the Open Project of Beijing Multimedia and Intelligent Software Key Laboratory in Beijing University of Technology, and the sponsor from Intel ICRI MNC project, in part to Dr. Hou-Qiang Li by the National Natural Science Foundation of China (NSFC) under Grant Nos.61325009,61390514, and 61272316, in part to Dr. Yijuan Lu by the Army Research Office of USA under Grant No. W911NF-12-1-0057 and the National Science Foundation of USA under Grant No. CRI 1305302, and in part to Dr. Qi Tian by ARO under Grant No. W911NF-12-1-0057 and the Faculty Research Award by NEC Laboratories of America, respectively. This work was supported in part by NSFC under Grant No.61128007.A preliminary version of the paper was published in the Proceedings of MM 2010 funderid: (ARO) of USA under Grant No. W911NF-12-1-0057 and the National Science Foundation of USA under Grant No. CRI 1305302, and in part to Dr. Qi Tian by ARO under Grant No. W911NF-12-1-0057 and the Faculty Research Award by NEC Laboratories of America, respectively. This work was supported in part by NSFC under Grant No.61128007.A preliminary version of the paper was published in the Proceedings of MM 2010 |
| GroupedDBID | -4Z -59 -5G -BR -EM -Y2 -~C .86 .VR 06D 0R~ 0VY 1N0 1SB 2.D 28- 29K 2B. 2C0 2J2 2JN 2JY 2KG 2KM 2LR 2RA 2VQ 2~H 30V 3V. 4.4 406 408 409 40D 40E 5GY 5QI 5VR 5VS 67Z 6NX 7WY 8FE 8FG 8FL 8TC 8UJ 92H 92I 92L 92R 93N 95- 95. 95~ 96X AAAVM AABHQ AABYN AAFGU AAHNG AAIAL AAJKR AANZL AAOBN AARHV AARTL AATNV AATVU AAUYE AAWCG AAWWR AAYFA AAYIU AAYQN AAYTO ABBBX ABBXA ABDZT ABECU ABFGW ABFTD ABFTV ABHLI ABHQN ABJCF ABJNI ABJOX ABKAS ABKCH ABKTR ABMNI ABMQK ABNWP ABQBU ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABUWG ABWNU ABXPI ACAOD ACBMV ACBRV ACBXY ACGFS ACHSB ACHXU ACIGE ACIPQ ACKNC ACMDZ ACMLO ACOKC ACOMO ACSNA ACTTH ACVWB ACWMK ACZOJ ADGRI ADHHG ADHIR ADINQ ADKNI ADKPE ADMDM ADOXG ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEEQQ AEFIE AEFTE AEGAL AEGNC AEJHL AEJRE AEKMD AENEX AEOHA AEPYU AESKC AESTI AETLH AEVLU AEVTX AEXYK AEYWE AFEXP AFGCZ AFKRA AFLOW AFNRJ AFQWF AFUIB AFWTZ AFZKB AGAYW AGDGC AGGBP AGGDS AGJBK AGMZJ AGQMX AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHSBF AHYZX AIAKS AIIXL AILAN AIMYW AITGF AJBLW AJDOV AJRNO ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMXSW AMYLF AMYQR AOCGG ARAPS ARMRJ ASPBG AVWKF AXYYD AZFZN AZQEC B-. BA0 BBWZM BDATZ BENPR BEZIV BGLVJ BGNMA BPHCQ CAG CCEZO CCPQU CHBEP COF CQIGP CS3 CSCUP CUBFJ CW9 D-I DDRTE DNIVK DPUIP DU5 DWQXO EBLON EBS EIOEI EJD ESBYG F5P FA0 FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRNLG FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNUQQ GNWQR GQ6 GQ7 GQ8 GROUPED_ABI_INFORM_COMPLETE GXS HCIFZ HF~ HG6 HMJXF HQYDN HRMNR HVGLF HZ~ IAO IHE IJ- IKXTQ IWAJR IXC IXD IXE IZIGR IZQ I~X I~Z J-C JBSCW JCJTX JZLTJ K60 K6V K6~ K7- KDC KOV LAK LLZTM M0C M0N M4Y M7S MA- N2Q NB0 NDZJH NF0 NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OAM P19 P2P P62 P9O PF0 PQBIZ PQQKQ PROAC PT4 PT5 PTHSS Q2X QOK QOS R4E R89 R9I RHV RNI RNS ROL RPX RSV RZK S16 S1Z S26 S27 S28 S3B SAP SCJ SCL SCLPG SCO SDH SDM SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 T16 TCJ TGT TSG TSK TSV TUC U2A UG4 UNUBA UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 W92 WK8 YLTOR Z7R Z7U Z7X Z7Z Z81 Z83 Z88 Z8R Z8W Z92 ZMTXR ~A9 ~EX ~WA -SI -S~ 5XA 5XJ AACDK AAJBT AASML AAXDM AAYZH ABAKF ABQSL ACDTI ACPIV AEFQL AEMSY AFBBN AGQEE AGRTI AIGIU BSONS CAJEI H13 PQBZA Q-- U1G U5S AAPKM AAYXX ABBRH ABDBE ABFSG ACSTC ADHKG AEZWR AFDZB AFHIU AFOHR AGQPQ AHPBZ AHWEU AIXLP ATHPR AYFIA CITATION ICD IVC PHGZM PHGZT TGMPQ 7SC 7XB 8AL 8FD 8FK ABRTQ JQ2 L.- L6V L7M L~C L~D PKEHL PQEST PQGLB PQUKI PRINS Q9U PUEGO 4A8 PMFND PSX |
| ID | FETCH-LOGICAL-c413t-1841c3a99edec507d6b1c2ffc06f0f68c555806870a85a57f02c95d2a4b6bc7d3 |
| IEDL.DBID | U2A |
| ISSN | 1000-9000 |
| IngestDate | Thu May 29 04:00:15 EDT 2025 Thu Sep 04 17:43:39 EDT 2025 Sat Aug 23 14:04:53 EDT 2025 Thu Apr 24 23:11:07 EDT 2025 Sun Jul 06 05:08:13 EDT 2025 Fri Feb 21 02:40:04 EST 2025 Wed Feb 14 10:36:31 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 5 |
| Keywords | affine estimation spatial verification spatial context coding large-scale image retrieval |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c413t-1841c3a99edec507d6b1c2ffc06f0f68c555806870a85a57f02c95d2a4b6bc7d3 |
| Notes | 11-2296/TP Wen-Gang Zhou,Hou-Qiang Li,Yijuan Lu,Qi Tian（1 Chinese Academy of Sciences Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China, Hefei 230027, China; 2Department of Electronic Engineering and Information Science, University of Science and Technology of China Hefei 230027, China ;3Department of Computer Science, Texas State University, San Marcos, TX 78666, U.S.A. ;4Department of Computer Science, University of Texas at San Antonio, San Antonio, TX 78249, U.S.A.） large-scale image retrieval; spatial context coding; spatial verification; affine estimation Many recent state-of-the-art image retrieval approaches are based on Bag-of-Visual-Words model and represent an image with a set of visual words by quantizing local SIFT（scale invariant feature transform） features. Feature quantization reduces the discriminative power of local features and unavoidably causes many false local matches between images, which degrades the retrieval accuracy. To filter those false matches, geometric context among visual words has been popularly explored for the verification of geometric consistency. However, existing studies with global or local geometric verification are either computationally expensive or achieve limited accuracy. To address this issue, in this paper, we focus on partialduplicate Web image retrieval, and propose a scheme to encode the spatial context for visual matching verification. An efficient affine enhancement scheme is proposed to refine the verification results. Experiments on partial-duplicate Web image search, using a database of one million images, demonstrate the effectiveness and efficiency of the proposed approach.Evaluation on a 10-million image database further reveals the scalability of our approach. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| PQID | 2918576140 |
| PQPubID | 326258 |
| PageCount | 12 |
| ParticipantIDs | wanfang_journals_jsjkxjsxb_e201405010 proquest_miscellaneous_1629369348 proquest_journals_2918576140 crossref_citationtrail_10_1007_s11390_014_1472_3 crossref_primary_10_1007_s11390_014_1472_3 springer_journals_10_1007_s11390_014_1472_3 chongqing_primary_662608753 |
| PublicationCentury | 2000 |
| PublicationDate | 2014-09-01 |
| PublicationDateYYYYMMDD | 2014-09-01 |
| PublicationDate_xml | – month: 09 year: 2014 text: 2014-09-01 day: 01 |
| PublicationDecade | 2010 |
| PublicationPlace | Boston |
| PublicationPlace_xml | – name: Boston – name: Beijing |
| PublicationTitle | Journal of computer science and technology |
| PublicationTitleAbbrev | J. Comput. Sci. Technol |
| PublicationTitleAlternate | Journal of Computer Science and Technology |
| PublicationTitle_FL | Journal of Computer Science & Technology |
| PublicationYear | 2014 |
| Publisher | Springer US Springer Nature B.V |
| Publisher_xml | – name: Springer US – name: Springer Nature B.V |
| References | Zheng L,Wang S, Liu Z, Tian Q. LP-Norm IDF for large scale image search. In Proc. CVPR, June 2013, pp.1626–1633. Deng J, Dong W, Socher R et al. ImageNet: A large-scale hierarchical image database. In Proc. CVPR, June 2009, pp.248–255. ChuLJiangSWangSZhangYHuangQRobust spatial consistency graph model for partial duplicate image retrievalIEEE Trans. Multimedia20131581982199610.1109/TMM.2013.2270455 Nister D, Stewenius H. Scalable recognition with a vocabulary tree. In Proc. CVRP, June 2006, pp.2161–2168. Babenko A, Lempitsky V. The inverted multi-index. In Proc. CVPR, June 2012, pp.3069–3076. Chum O, Philbin J, Sivic J, Isard M, Zisserman A. Total recall: Automatic query expansion with a generative featuremodel for object retrieval. In Proc. the 11th IEEE Int. Conf. Computer Vision, Oct. 2007, pp.1–8. ChangSShiQYanCIconic indexing by 2-D stringsIEEE Trans. Pattern Analysis and Machine Intelligence19879341332810.1109/TPAMI.1987.4767923 Zhang Y, Chen T. Efficient kernels for identifying unbounded-order spatial features. In Proc. CVPR, June 2009, pp.1762–1769. Zhang S, Yang M, Wang X et al. Semantic-aware co-indexing for image retrieval. In Proc. ICCV, 2013, pp.1673–1680. Zhou W, Li H, Lu Y, Wang M, Tian Q. Visual word expansion and BSIFT verification for large-scale image search. Multimedia Systems, 2013. http://link.springer.com/article/10.1007/s00530-013-0330-4, Aug. 2014. WangWZhangDZhangYLiJGuXRobust spatial matching for object retrieval and its parallel implementation on GPUIEEE Trans. Multimedia20111361308131810.1109/TMM.2011.2165053 Savarese S, Winn J, Criminisi A. Discriminative object class models of appearance and shape by correlatons. In Proc. CVPR, June 2006, pp.2033–2040. Sivic J, Zisserman A. Video Google: A text retrieval approach to object matching in videos. In Proc. the 9th IEEE Int. Conf. Computer Vision, Oct. 2003, pp.1470–1477. Philbin J, Chum O, Isard M et al. Lost in quantization: Improving particular object retrieval in large scale image databases. In Proc. CVPR, June 2008, pp.1–8. XieLTianQZhouWFast and accurate near-duplicate image search with affinity propagation on the Image WebComputer Vision and Image Understanding2014124314110.1016/j.cviu.2013.12.011 Lazebnik S, Schmid C, Ponce J. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In Proc. CVPR, June 2006, pp.2169–2178. Wu Z, Ke Q, Isard M, Sun J. Bundling features for large scale partial-duplicate web image search. In Proc. CVPR, June 2009, pp.25–32. Zhang Y, Jia Z, Chen T. Image retrieval with geometry-preserving visual phrases. In Proc. CVPR, June 2011, pp.809–816. Zhang X, Zhang L, Shum H Y. QsRank: Query-sensitive hash code ranking for efficient 2-neighbor search. In Proc. CVPR, June 2012, pp.2058–2065. Smith J R, Chang S F. VisualSEEk: A fully automated content-based image query system. In Proc. the 4th ACM Multimedia, Nov. 1996, pp.75–84. Chum O, Perdoch M, Matas J. Geometric min-hashing: Finding a (thick) needle in a haystack. In Proc. CVPR, June 2009, pp.17–24. Arandjelovic R, Zisserman A. Three things everyone should know to improve object retrieval. In Proc. CVPR, June 2012, pp.2911–2918. Chum O, Mikulik A, Perdoch M, Matas J. Total recall II: Query expansion revisited. In Proc. CVPR, June 2011, pp.889–896. Jégou H, Harzallah H, Schmid C. A contextual dissimilarity measure for accurate and efficient image search. In Proc. CVPR, June 2007, pp.1–8. LiuZLiHZhouWZhaoRTianQContextual hashing for large-scale image searchIEEE Trans. Image Processing201423416061614319131810.1109/TIP.2014.2305072 XieHGaoKZhangYEfficient feature detection and effective post-verification for large scale near-duplicate image searchIEEE Trans. Multimedia20111361319133210.1109/TMM.2011.2167224 Jégou H, Douze M, Schmid C, Pérez P. Aggregating local descriptors into a compact image representation. In Proc. CVPR, June 2010, pp.3304–3311. Zhou W, Lu Y, Li H, Song Y, Tian Q. Spatial coding for large scale partial-duplicate Web image search. In Proc. Int. Conf. Multimedia, Oct. 2010, pp.511–520. LoweDGDistinctive image features from scale invariant keypointsInternational Journal of Computer Vision20046029111010.1023/B:VISI.0000029664.99615.94 Philbin J, Chum O, Isard M, Sivic J, Zisserman A. Object retrieval with large vocabularies and fast spatial matching. In Proc. CVPR, June 2007, pp.1–8. BelongieSMalikJPuzichaJShape matching and object recognition using shape contextsIEEE Trans. Pattern Analysis and Machine Intelligence200224450952210.1109/34.993558 Chum O, Philbin J, Zisserman A. Near duplicate image detection: Min-Hash and tf-idf weighting. In Proc. the 19th BMVC, Sept. 2008, pp.493–502. Jegou H, Douze M, Schmid C. Hamming embedding and weak geometric consistency for large scale image search. In Proc. the 10th ECCV, Oct. 2008, pp.304–317. MatasJChumOUrbanMPajdlaTRobust wide-baseline stereo from maximally stable extremal regionsImage and Vision Computing2004221076176710.1016/j.imavis.2004.02.006 ZhouWYangMLiHWangXLinYTianQTowards codebook-free: Scalable cascaded hashing for mobile image searchIEEE Trans. Multimedia201416360161110.1109/TMM.2014.2301979 Chum O, Matas J. Matching with PROSAC-progressive sample consensus. In Proc. CVPR, June 2005, pp.220–226. Shen X, Lin Z, Brandt J et al. Object retrieval and localization with spatially-constrained similarity measure and k-NN re-ranking. In Proc. CVPR, June 2012, pp.3013–3020. Chum O, Philbin J, Isard M et al. Scalable near identical image and shot detection. In Proc. CIVR, July 2007, pp.549–556. FischlerMABollesRCRandom sample consensus: A paradigm for model fitting with applications to image analysis and automated cartographyCommunications of the ACM198124638139561815810.1145/358669.358692 Zhou W, Lu Y, Li H, Tian Q. Scalar quantization for large scale image search. In Proc. the 20th ACM Multimedia, Oct. 2012, pp.169–178. Zhou W, Li H, Lu Y, Tian Qi. SIFT match verification by geometric coding for large-scale partial-duplicate Web image search. ACM Trans. Multimedia Computing, Communications, and Applications, 2013, 9(1): Article No. 4. XieHZhangYTanJGuoLLiJContextual query expansion for image retrievalIEEE Trans. Multimedia20141641104111410.1109/TMM.2014.2305909 Zhang S, Tian Q, Lu K et al. Edge-SIFT: Discriminative binary descriptor for scalable partial-duplicate mobile search. IEEE Trans. Image Processing, 2013, 22(7): pp.2889–2902. Yuan J, Wu Y, Yang M. Discovery of collocation patterns: From visual words to visual phrases. In Proc. CVPR, June 2007, pp.1–8. 1472_CR5 L Xie (1472_CR3) 2014; 124 1472_CR6 MA Fischler (1472_CR33) 1981; 24 1472_CR7 1472_CR1 1472_CR12 1472_CR34 1472_CR11 1472_CR14 1472_CR13 1472_CR35 1472_CR8 1472_CR16 1472_CR38 1472_CR9 1472_CR15 DG Lowe (1472_CR19) 2004; 60 1472_CR39 W Zhou (1472_CR30) 2014; 16 J Matas (1472_CR37) 2004; 22 1472_CR10 1472_CR32 1472_CR31 S Chang (1472_CR36) 1987; 9 L Chu (1472_CR4) 2013; 15 1472_CR23 W Wang (1472_CR24) 2011; 13 1472_CR22 1472_CR44 1472_CR25 1472_CR27 1472_CR26 H Xie (1472_CR2) 2011; 13 1472_CR29 1472_CR28 S Belongie (1472_CR40) 2002; 24 1472_CR41 1472_CR21 1472_CR43 1472_CR20 1472_CR42 H Xie (1472_CR17) 2014; 16 Z Liu (1472_CR18) 2014; 23 |
| References_xml | – reference: Nister D, Stewenius H. Scalable recognition with a vocabulary tree. In Proc. CVRP, June 2006, pp.2161–2168. – reference: Arandjelovic R, Zisserman A. Three things everyone should know to improve object retrieval. In Proc. CVPR, June 2012, pp.2911–2918. – reference: Zhang X, Zhang L, Shum H Y. QsRank: Query-sensitive hash code ranking for efficient 2-neighbor search. In Proc. CVPR, June 2012, pp.2058–2065. – reference: Shen X, Lin Z, Brandt J et al. Object retrieval and localization with spatially-constrained similarity measure and k-NN re-ranking. In Proc. CVPR, June 2012, pp.3013–3020. – reference: Zhang Y, Chen T. Efficient kernels for identifying unbounded-order spatial features. In Proc. CVPR, June 2009, pp.1762–1769. – reference: Chum O, Philbin J, Zisserman A. Near duplicate image detection: Min-Hash and tf-idf weighting. In Proc. the 19th BMVC, Sept. 2008, pp.493–502. – reference: Chum O, Matas J. Matching with PROSAC-progressive sample consensus. In Proc. CVPR, June 2005, pp.220–226. – reference: Chum O, Philbin J, Isard M et al. Scalable near identical image and shot detection. In Proc. CIVR, July 2007, pp.549–556. – reference: ChangSShiQYanCIconic indexing by 2-D stringsIEEE Trans. Pattern Analysis and Machine Intelligence19879341332810.1109/TPAMI.1987.4767923 – reference: Jegou H, Douze M, Schmid C. Hamming embedding and weak geometric consistency for large scale image search. In Proc. the 10th ECCV, Oct. 2008, pp.304–317. – reference: Chum O, Mikulik A, Perdoch M, Matas J. Total recall II: Query expansion revisited. In Proc. CVPR, June 2011, pp.889–896. – reference: Zhou W, Lu Y, Li H, Tian Q. Scalar quantization for large scale image search. In Proc. the 20th ACM Multimedia, Oct. 2012, pp.169–178. – reference: MatasJChumOUrbanMPajdlaTRobust wide-baseline stereo from maximally stable extremal regionsImage and Vision Computing2004221076176710.1016/j.imavis.2004.02.006 – reference: Zhou W, Lu Y, Li H, Song Y, Tian Q. Spatial coding for large scale partial-duplicate Web image search. In Proc. Int. Conf. Multimedia, Oct. 2010, pp.511–520. – reference: XieHGaoKZhangYEfficient feature detection and effective post-verification for large scale near-duplicate image searchIEEE Trans. Multimedia20111361319133210.1109/TMM.2011.2167224 – reference: Zhou W, Li H, Lu Y, Tian Qi. SIFT match verification by geometric coding for large-scale partial-duplicate Web image search. ACM Trans. Multimedia Computing, Communications, and Applications, 2013, 9(1): Article No. 4. – reference: Lazebnik S, Schmid C, Ponce J. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In Proc. CVPR, June 2006, pp.2169–2178. – reference: Philbin J, Chum O, Isard M, Sivic J, Zisserman A. Object retrieval with large vocabularies and fast spatial matching. In Proc. CVPR, June 2007, pp.1–8. – reference: XieLTianQZhouWFast and accurate near-duplicate image search with affinity propagation on the Image WebComputer Vision and Image Understanding2014124314110.1016/j.cviu.2013.12.011 – reference: XieHZhangYTanJGuoLLiJContextual query expansion for image retrievalIEEE Trans. Multimedia20141641104111410.1109/TMM.2014.2305909 – reference: ZhouWYangMLiHWangXLinYTianQTowards codebook-free: Scalable cascaded hashing for mobile image searchIEEE Trans. Multimedia201416360161110.1109/TMM.2014.2301979 – reference: FischlerMABollesRCRandom sample consensus: A paradigm for model fitting with applications to image analysis and automated cartographyCommunications of the ACM198124638139561815810.1145/358669.358692 – reference: Philbin J, Chum O, Isard M et al. Lost in quantization: Improving particular object retrieval in large scale image databases. In Proc. CVPR, June 2008, pp.1–8. – reference: Zhou W, Li H, Lu Y, Wang M, Tian Q. Visual word expansion and BSIFT verification for large-scale image search. Multimedia Systems, 2013. http://link.springer.com/article/10.1007/s00530-013-0330-4, Aug. 2014. – reference: Savarese S, Winn J, Criminisi A. Discriminative object class models of appearance and shape by correlatons. In Proc. CVPR, June 2006, pp.2033–2040. – reference: WangWZhangDZhangYLiJGuXRobust spatial matching for object retrieval and its parallel implementation on GPUIEEE Trans. Multimedia20111361308131810.1109/TMM.2011.2165053 – reference: Sivic J, Zisserman A. Video Google: A text retrieval approach to object matching in videos. In Proc. the 9th IEEE Int. Conf. Computer Vision, Oct. 2003, pp.1470–1477. – reference: Chum O, Philbin J, Sivic J, Isard M, Zisserman A. Total recall: Automatic query expansion with a generative featuremodel for object retrieval. In Proc. the 11th IEEE Int. Conf. Computer Vision, Oct. 2007, pp.1–8. – reference: Jégou H, Douze M, Schmid C, Pérez P. Aggregating local descriptors into a compact image representation. In Proc. CVPR, June 2010, pp.3304–3311. – reference: Zhang S, Yang M, Wang X et al. Semantic-aware co-indexing for image retrieval. In Proc. ICCV, 2013, pp.1673–1680. – reference: Smith J R, Chang S F. VisualSEEk: A fully automated content-based image query system. In Proc. the 4th ACM Multimedia, Nov. 1996, pp.75–84. – reference: Babenko A, Lempitsky V. The inverted multi-index. In Proc. CVPR, June 2012, pp.3069–3076. – reference: LoweDGDistinctive image features from scale invariant keypointsInternational Journal of Computer Vision20046029111010.1023/B:VISI.0000029664.99615.94 – reference: Chum O, Perdoch M, Matas J. Geometric min-hashing: Finding a (thick) needle in a haystack. In Proc. CVPR, June 2009, pp.17–24. – reference: Wu Z, Ke Q, Isard M, Sun J. Bundling features for large scale partial-duplicate web image search. In Proc. CVPR, June 2009, pp.25–32. – reference: LiuZLiHZhouWZhaoRTianQContextual hashing for large-scale image searchIEEE Trans. Image Processing201423416061614319131810.1109/TIP.2014.2305072 – reference: Zhang Y, Jia Z, Chen T. Image retrieval with geometry-preserving visual phrases. In Proc. CVPR, June 2011, pp.809–816. – reference: Zheng L,Wang S, Liu Z, Tian Q. LP-Norm IDF for large scale image search. In Proc. CVPR, June 2013, pp.1626–1633. – reference: BelongieSMalikJPuzichaJShape matching and object recognition using shape contextsIEEE Trans. Pattern Analysis and Machine Intelligence200224450952210.1109/34.993558 – reference: Zhang S, Tian Q, Lu K et al. Edge-SIFT: Discriminative binary descriptor for scalable partial-duplicate mobile search. IEEE Trans. Image Processing, 2013, 22(7): pp.2889–2902. – reference: ChuLJiangSWangSZhangYHuangQRobust spatial consistency graph model for partial duplicate image retrievalIEEE Trans. Multimedia20131581982199610.1109/TMM.2013.2270455 – reference: Deng J, Dong W, Socher R et al. ImageNet: A large-scale hierarchical image database. In Proc. CVPR, June 2009, pp.248–255. – reference: Jégou H, Harzallah H, Schmid C. A contextual dissimilarity measure for accurate and efficient image search. In Proc. CVPR, June 2007, pp.1–8. – reference: Yuan J, Wu Y, Yang M. Discovery of collocation patterns: From visual words to visual phrases. In Proc. CVPR, June 2007, pp.1–8. – ident: 1472_CR28 doi: 10.1109/TIP.2013.2251650 – ident: 1472_CR32 doi: 10.1109/CVPR.2012.6247910 – ident: 1472_CR15 doi: 10.1145/1873951.1874019 – ident: 1472_CR5 doi: 10.1109/ICCV.2003.1238663 – ident: 1472_CR31 doi: 10.1109/CVPR.2012.6248018 – ident: 1472_CR41 doi: 10.1109/CVPR.2006.102 – ident: 1472_CR42 doi: 10.1109/CVPR.2007.383222 – volume: 23 start-page: 1606 issue: 4 year: 2014 ident: 1472_CR18 publication-title: IEEE Trans. Image Processing doi: 10.1109/TIP.2014.2305072 – ident: 1472_CR43 doi: 10.1109/CVPR.2009.5206791 – volume: 13 start-page: 1319 issue: 6 year: 2011 ident: 1472_CR2 publication-title: IEEE Trans. Multimedia doi: 10.1109/TMM.2011.2167224 – ident: 1472_CR10 doi: 10.1007/978-3-540-88682-2_24 – ident: 1472_CR22 – ident: 1472_CR39 doi: 10.1109/CVPR.2006.68 – ident: 1472_CR1 doi: 10.1109/CVPR.2009.5206566 – ident: 1472_CR27 doi: 10.1109/ICCV.2013.210 – ident: 1472_CR26 – ident: 1472_CR6 doi: 10.1109/CVPR.2006.264 – volume: 15 start-page: 1982 issue: 8 year: 2013 ident: 1472_CR4 publication-title: IEEE Trans. Multimedia doi: 10.1109/TMM.2013.2270455 – ident: 1472_CR23 doi: 10.1145/2422956.2422960 – ident: 1472_CR20 doi: 10.1145/2393347.2393377 – ident: 1472_CR25 doi: 10.1109/CVPR.2011.5995601 – volume: 124 start-page: 31 year: 2014 ident: 1472_CR3 publication-title: Computer Vision and Image Understanding doi: 10.1016/j.cviu.2013.12.011 – ident: 1472_CR16 doi: 10.1109/CVPR.2013.213 – volume: 9 start-page: 413 issue: 3 year: 1987 ident: 1472_CR36 publication-title: IEEE Trans. Pattern Analysis and Machine Intelligence doi: 10.1109/TPAMI.1987.4767923 – ident: 1472_CR12 doi: 10.1109/CVPR.2008.4587635 – volume: 22 start-page: 761 issue: 10 year: 2004 ident: 1472_CR37 publication-title: Image and Vision Computing doi: 10.1016/j.imavis.2004.02.006 – ident: 1472_CR7 doi: 10.1109/ICCV.2007.4408891 – volume: 24 start-page: 509 issue: 4 year: 2002 ident: 1472_CR40 publication-title: IEEE Trans. Pattern Analysis and Machine Intelligence doi: 10.1109/34.993558 – ident: 1472_CR9 doi: 10.1109/CVPRW.2009.5206531 – ident: 1472_CR38 doi: 10.1145/1282280.1282359 – ident: 1472_CR44 doi: 10.1109/CVPR.2009.5206848 – volume: 16 start-page: 1104 issue: 4 year: 2014 ident: 1472_CR17 publication-title: IEEE Trans. Multimedia doi: 10.1109/TMM.2014.2305909 – ident: 1472_CR11 doi: 10.1109/CVPR.2007.383172 – ident: 1472_CR29 doi: 10.1109/CVPR.2007.382970 – volume: 13 start-page: 1308 issue: 6 year: 2011 ident: 1472_CR24 publication-title: IEEE Trans. Multimedia doi: 10.1109/TMM.2011.2165053 – ident: 1472_CR34 doi: 10.1109/CVPR.2005.221 – ident: 1472_CR35 doi: 10.1145/244130.244151 – volume: 24 start-page: 381 issue: 6 year: 1981 ident: 1472_CR33 publication-title: Communications of the ACM doi: 10.1145/358669.358692 – ident: 1472_CR8 doi: 10.5244/C.22.50 – ident: 1472_CR13 doi: 10.1109/CVPR.2010.5540039 – volume: 60 start-page: 91 issue: 2 year: 2004 ident: 1472_CR19 publication-title: International Journal of Computer Vision doi: 10.1023/B:VISI.0000029664.99615.94 – volume: 16 start-page: 601 issue: 3 year: 2014 ident: 1472_CR30 publication-title: IEEE Trans. Multimedia doi: 10.1109/TMM.2014.2301979 – ident: 1472_CR21 doi: 10.1109/CVPR.2012.6248038 – ident: 1472_CR14 doi: 10.1109/CVPR.2011.5995528 |
| SSID | ssj0037044 |
| Score | 2.0016596 |
| Snippet | Many recent state-of-the-art image retrieval approaches are based on Bag-of-Visual-Words model and represent an image with a set of visual words by quantizing... |
| SourceID | wanfang proquest crossref springer chongqing |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 837 |
| SubjectTerms | Accuracy Artificial Intelligence Computer Science Context Data Structures and Information Theory Geometric accuracy Image retrieval Information Systems Applications (incl.Internet) Invariants Regular Paper Reproduction Retrieval Searching Software Engineering Theory of Computation Verification Visual Web 一致性验证 图像数据库 图像检索方法 复制 空间环境 编码 视觉匹配 |
| SummonAdditionalLinks | – databaseName: ProQuest Technology Collection dbid: 8FG link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1Lb9QwELagXLjwKojtS64EF5BFYjvO-oRQ2-1DUFVARW-W7dittiVpya60P5-ZrLOBy55jO1Fm7PnG8_gIecd9YZ2PWPkrBJOl0kxHVzKEFjbTThU5ViN_O1cnl_LsqrhKF25tSqvsz8TuoK4aj3fkn7jGrkVgTLLP9w8MWaMwupooNB6TJzkHW4uV4pPj_iQWZdaRueIVNkNyzD6q2ZXOAfTBlCzJcllyJrC3wk1TXz-AxfjfRg3AcxUr7Sp86mjr63-M0eQFeZZQJP2yFPtL8ijUr8jznqGBpg27Sc6Pat-gdaJIPQyqRrtuVIsZBaxKv2IWOPsBUgr0AlXI3rHD-TKgHeiv4Ojpbzhu6PeOdQtU8jW5nBz9PDhhiUGBeTBOMwbuW-6F1TpUwQPyq5TLPY_RZypmUY09dvvKFOxZOy5sUcaMe11U3EqnnC8r8YZs1E0d3hJaCuFdFUJUrpTRay1lrCKAi0JHwAT5iGyv_p-5X3bKMArdJfSIRiTr_6jxqfk4cmDcmaFtMgrEgEAMCsTAlA-rKf16awbv9GIyaRO2ZlCZEdlfPYbtgzERW4dm3ppccaQ0FHI8Ih978Q5LrHnh-6QBw-BpO71dTNuFM4Gj41qAr7u1_sO2yVMcukxa2yEbsz_zsAsoZ-b2OlX-Cx8m9Xo priority: 102 providerName: ProQuest |
| Title | Encoding Spatial Context for Large-Scale Partial-Duplicate Web Image Retrieval |
| URI | http://lib.cqvip.com/qk/85226X/201405/662608753.html https://link.springer.com/article/10.1007/s11390-014-1472-3 https://www.proquest.com/docview/2918576140 https://www.proquest.com/docview/1629369348 https://d.wanfangdata.com.cn/periodical/jsjkxjsxb-e201405010 |
| Volume | 29 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3db9MwELfY9sIL34iyURkJXkCWEjt26scC7cZXNQ0qxpNlO_amMlIgrdQ_n7s0bkBCk3jKQ-Kz5Lvz_S73Rcgz7qV1PmLlrxCsKJVmOrqSIbSwmXZK5liN_HGmTubFu3N53tVxNynbPYUk25u6L3YDsIJJVAXLi5IzsUcOJLaTAiGe83G6fkWZtRNc8b81w4mYKZT5LxLYUOFyWV_8hO3-Nkw92twFSNuynjra-uIPCzS9Q2510JGOt7y-S26E-h65ncYy0E5L75PZpPZLNEkU5w2DfNG2BdVmRQGg0g-Y-s0-AWsCPUW5sVfszXobxQ70S3D07Xe4Y-hZO2oL5PABmU8nn1-fsG5sAvNgkVYMfLbcC6t1qIIHuFcpl3seo89UzKIaeWzxlSlQVDuSVpYx417LitvCKefLSjwk-_WyDo8ILYXwrgohKlcW0WtdFLGKgCikjgAE8gE53J2f-bFtj2EU-kjoBg1Ilk7U-K7jOA6-uDJ9r2RkiAGGGGSIgSUvdksSvWs-PkpsMp3mNYZr7G4FoCMbkKe716AzGAixdViuG5MrjnMMRTEakJeJvT2JazZ83klA__GiWXzbLJqNM4GjtyrBwX38X1QPyU1cuU1cOyL7q1_r8ASQzsoNyd5oejwkB-Pjr-8n8Hw1mZ2eDVt5_w1ycPYN |
| linkProvider | Springer Nature |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3fb9MwELZG9wAv_EaUDTASewFZJE7i1A8TAtapZV01jU3szbMde6iMZCOtKP8cfxt3adzCS9_2nNiJfOe773y--wh5zW2mjfVY-ZskLM2FZNKbnCG00JE0IouxGvlwLAan6eez7GyD_Am1MHitMtjExlAXlcUz8ndcYtcicCbR-6trhqxRmF0NFBq6pVYodpsWY21hx4H7_QtCuHp3uAfy3uF8v3_yacBalgFmwYBPGYQ4sU20lK5wFtBRIUxsufc2Ej7yomexI1YkQK91L9NZ7iNuZVZwnRphbF4kMO8tspniAUqHbH7sj4-Ogy9I8qihk8VDdIb0nCGv2hTvAfjCS2Epi9OcswS7O3yryotr8Fn_e8kV9F1ma5sao9Lr8uIfd7h_n9xtcSz9sFC8B2TDlQ_JvcARQVuT8YiM-6Wt0D9SJD8GZadNP6z5lAJapiO8h86-gJ44eoRKrC_Z3myRUnf0qzN0-AMMHj1ueL9gUzwmpzeyuk9Ip6xK95TQPEmsKZzzwuSpt1KmqS88wJtMekAlcZdsLddPXS16dSiBARvGZF0ShRVVtm1_jiwcl2rVuBkFokAgCgWiYMib5ZAw35qXt4OYVGsGarVS2i55tXwMGxizMrp01axWseBIqpikvS55G8S7mmLNB3daDVi9PKkn3-eTem6U4xg6ZxBtP1v_Yy_J7cHJ4UiNhuODLXIHhy2u0G2TzvTnzD0HzDU1L1rFpuT8pvfSX_2MOSo |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3fb9MwED5BJyFeGD9F2QAjwQvIW344Tv04sZaNjWoCJsaTFzv2UDfSQVKp4q_nrolbQGgS4jnJRbHP9ne5u-8DeJ7YrDDWU-dvmnKRS8WVNzknaFFEysgspm7kd2O5dyzenmQnnc5pHardQ0qy7Wkglqaq2b4s_faq8Q2BCxVUCR6LPOHpdVgTJCHRg7WdN58PhmEzTvNooedKf7E56WOGxObfjBC9wpdpdfYNX_77MbXCnst06aLJp_JFdfbLeTRah9PwJW0ZyvnWrDFb9scfJI__8am34VaHVdlO61x34Jqr7sJ60IFg3bZwD8bDyk7pDGQkcIwOzRacV_OGISJmh1Rrzj-gLzh2RI5aXPDdWZs2d-yTM2z_K25q7P1C2wsd_z4cj4YfX-_xTqeBWzwCG45BYmzTQilXOov4spQmton3NpI-8nJgiVMskrgzFIOsyHIfJVZlZVIII43Ny_QB9Kpp5R4Cy9PUmtI5L00uvFVKCF96hDCZ8og84j5sLKdIX7Z8HFpSUEZxVx-iMGnadhTnpLRxoVfkzDSaGkdT02hqfOTl8pFg74qbN4Mn6G6p1zpRRKeFKCfqw7PlZVyklHkpKjed1TqWCQknpmLQh1dhylcmrnjhi87JVjdP6sn5fFLPjXYJhccZRtSP_snqU7hxtDvSh_vjgw24SUbaorlN6DXfZ-4xoqzGPOlW0k_9KxqK |
| 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=Encoding+Spatial+Context+for+Large-Scale+Partial-Duplicate+Web+Image+Retrieval&rft.jtitle=%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%A7%91%E5%AD%A6%E6%8A%80%E6%9C%AF%E5%AD%A6%E6%8A%A5%EF%BC%88%E8%8B%B1%E6%96%87%E7%89%88%EF%BC%89&rft.au=Wen-Gang+Zhou&rft.au=Hou-Qiang+Li&rft.au=Yijuan+Lu&rft.au=Qi+Tian&rft.date=2014-09-01&rft.issn=1000-9000&rft.issue=5&rft.spage=837&rft.epage=848&rft_id=info:doi/10.1007%2Fs11390-014-1472-3&rft.externalDocID=jsjkxjsxb_e201405010 |
| thumbnail_s | http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=http%3A%2F%2Fimage.cqvip.com%2Fvip1000%2Fqk%2F85226X%2F85226X.jpg http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=http%3A%2F%2Fwww.wanfangdata.com.cn%2Fimages%2FPeriodicalImages%2Fjsjkxjsxb-e%2Fjsjkxjsxb-e.jpg |