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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPBW34M/3A3M7NE
Repositorysid.inpe.br/sibgrapi/2011/07.11.03.56
Last Update2011:07.11.03.56.14 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2011/07.11.03.56.14
Metadata Last Update2022:07.30.18.33.20 (UTC) administrator
DOI10.1109/SIBGRAPI.2011.36
Citation KeyCoelhoVallSantAraú:2011:SuClIn
TitleSubspace Clustering for Information Retrieval in Urban Scene Databases
FormatDVD, On-line.
Year2011
Access Date2024, Apr. 29
Number of Files1
Size23272 KiB
2. Context
Author1 Coelho, Marcelo de Miranda
2 Valle, Eduardo
3 dos Santos Júnior, Cássio Elias
4 Araújo, Arnaldo de Albuquerque
Affiliation1 Preparatory School of Air Cadets
2 University of Campinas
3 Federal University of Minas Gerais
4 Federal University of Minas Gerais
EditorLewiner, Thomas
Torres, Ricardo
e-Mail Addressmcoelho@dcc.ufmg.br
Conference NameConference on Graphics, Patterns and Images, 24 (SIBGRAPI)
Conference LocationMaceió, AL, Brazil
Date28-31 Aug. 2011
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2011-07-23 15:36:13 :: mcoelho@dcc.ufmg.br -> administrator :: 2011
2022-07-30 18:33:20 :: administrator -> :: 2011
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordssubspace clustering
information retrieval
large databases
urban databases
AbstractWe present a comprehensive study of two important subspace clustering algorithms and their contribution to enhance results for the difficult task of matching images of the same object using different devices at different conditions. Our experiments were performed on two distinct databases containing urban scenes which were tested using state-of-the-art matching algorithms. Our start point was the hypothesis that low discriminant local point descriptors lead to misclassification, which can be reduced employing clustering techniques as filters. A significantly amelioration of the results obtained for the two tested databases was achieved, which indicates that subspace clustering techniques have much to contribute at this kind of application. Another point is whether the occurrence of obstacles like trees and shadows are responsible for misclassification of images.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2011 > Subspace Clustering for...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Subspace Clustering for...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPBW34M/3A3M7NE
zipped data URLhttp://urlib.net/zip/8JMKD3MGPBW34M/3A3M7NE
Languageen
Target Filecoelho_sibgrapi_2011_camera_ready_final.pdf
User Groupmcoelho@dcc.ufmg.br
Visibilityshown
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/46SKNPE
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2022/05.15.00.56 7
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume


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