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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPBW34M/3C9TSSH
Repositorysid.inpe.br/sibgrapi/2012/07.15.17.58
Last Update2012:07.15.17.58.38 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2012/07.15.17.58.38
Metadata Last Update2022:06.14.00.07.33 (UTC) administrator
DOI10.1109/SIBGRAPI.2012.13
Citation KeyCuadrosBoteRodrBati:2012:SeLaIm
TitleSegmentation of Large Images with Complex Networks
FormatDVD, On-line.
Year2012
Access Date2024, May 05
Number of Files1
Size7080 KiB
2. Context
Author1 Cuadros, Oscar
2 Botelho, Glenda Michele
3 Rodrigues, Francisco
4 Batista Neto, João do Espírito Santo
Affiliation1 Universidade de São Paulo 
2 Universidade de São Paulo 
3 Universidade de São Paulo 
4 Universidade de São Paulo
EditorFreitas, Carla Maria Dal Sasso
Sarkar, Sudeep
Scopigno, Roberto
Silva, Luciano
e-Mail Addressglenda@icmc.usp.br
Conference NameConference on Graphics, Patterns and Images, 25 (SIBGRAPI)
Conference LocationOuro Preto, MG, Brazil
Date22-25 Aug. 2012
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2012-09-20 16:45:34 :: glenda@icmc.usp.br -> administrator :: 2012
2022-03-08 21:03:24 :: administrator -> menottid@gmail.com :: 2012
2022-03-10 12:52:59 :: menottid@gmail.com -> administrator :: 2012
2022-06-14 00:07:33 :: administrator -> :: 2012
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
KeywordsImage segmentation
complex networks and super pixels
AbstractImage segmentation is still a challenging issue in pattern recognition. Among the various segmentation approaches are those based on graph partitioning, which present some drawbacks, one being high processing times. With the recent developments on complex networks theory, pattern recognition techniques based on graphs have improved considerably. The identification of cluster of vertices can be considered a process of community identification according to complex networks theory. Since data clustering is related with image segmentation, image segmentation can also be approached via complex networks. However, image segmentation based on complex networks poses a fundamental limitation which is the excessive numbers of nodes in the network. This paper presents a complex network approach for large image segmentation that is both accurate and fast. To that, we incorporate the concept of super pixels, to reduce the number of nodes in the network. We evaluate our method for both synthetic and real images. Results show that our method can outperform other graph-based methods both in accuracy and processing times.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2012 > Segmentation of Large...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Segmentation of Large...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPBW34M/3C9TSSH
zipped data URLhttp://urlib.net/zip/8JMKD3MGPBW34M/3C9TSSH
Languageen
Target FilePaper.pdf
User Groupglenda@icmc.usp.br
Visibilityshown
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/46SL8GS
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2022/05.15.03.31 6
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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