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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPEW34M/43B52AP
Repositorysid.inpe.br/sibgrapi/2020/09.28.04.29
Last Update2020:09.28.04.37.43 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2020/09.28.04.29.48
Metadata Last Update2022:06.14.00.00.09 (UTC) administrator
DOI10.1109/SIBGRAPI51738.2020.00034
Citation KeyVargasBelizarioBati:2020:MuGrLa
TitleMulti-level Graph Label Propagation for Image Segmentation
FormatOn-line
Year2020
Access Date2024, Apr. 28
Number of Files1
Size5887 KiB
2. Context
Author1 Vargas Belizario, Ivar
2 Batista Neto, Joao
Affiliation1 University of São Paulo
2 University of São Paulo
EditorMusse, Soraia Raupp
Cesar Junior, Roberto Marcondes
Pelechano, Nuria
Wang, Zhangyang (Atlas)
e-Mail Addressl.ivarvb@gmail.com
Conference NameConference on Graphics, Patterns and Images, 33 (SIBGRAPI)
Conference LocationPorto de Galinhas (virtual)
Date7-10 Nov. 2020
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2020-09-28 04:37:44 :: l.ivarvb@gmail.com -> administrator :: 2020
2022-06-14 00:00:09 :: administrator -> l.ivarvb@gmail.com :: 2020
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordsimage segmentation
label propagation
complex networks
AbstractThis article introduces a multi-level automatic image segmentation method based on graphs and Label Propagation (LP), originally proposed for the detection of communities in complex networks, namely MGLP. To reduce the number of graph nodes, a super-pixel strategy is employed, followed by the computation of color descriptors. Segmentation is achieved by a deterministic propagation of vertex labels at each level. Several experiments with real color images of the BSDS500 dataset were performed to evaluate the method. Our method outperforms related strategies in terms of segmentation quality and processing time. Considering the Covering metric for image segmentation quality, for example, MGLP outperforms LPCI-SP, its most similar counterpart, in 38.99%. In term of processing times, MGLP is 1.07 faster than LPCI-SP.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2020 > Multi-level Graph Label...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Multi-level Graph Label...
doc Directory Contentaccess
source Directory Content
117.pdf 28/09/2020 01:29 5.7 MiB
agreement Directory Content
agreement.html 28/09/2020 01:29 1.2 KiB 
4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/43B52AP
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/43B52AP
Languageen
Target File117.pdf
User Groupl.ivarvb@gmail.com
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/43G4L9S
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2020/10.28.20.46 4
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination 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
7. Description control
e-Mail (login)l.ivarvb@gmail.com
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