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Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPBW34M/3C9U2C8
Repositorysid.inpe.br/sibgrapi/2012/07.15.18.28
Last Update2012:07.15.18.28.39 papa.joaopaulo@gmail.com
Metadatasid.inpe.br/sibgrapi/2012/07.15.18.28.39
Metadata Last Update2020:02.19.02.18.28 administrator
Citation KeyMansanoMaAfPaFaTo:2012:ImImCl
TitleImproving Image Classification Through Descriptor Combination
FormatDVD, On-line.
Year2012
Access Date2021, Jan. 28
Number of Files1
Size216 KiB
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Author1 Mansano, Alex Fernandes
2 Matsuoka, Jessica Akemi
3 Afonso, Luis Claudio Sugi
4 Papa, Joao Paulo
5 Faria, Fabio
6 Torres, Ricardo da Silva
Affiliation1 UNESP - Univ Estadual Paulista
2 UNESP - Univ Estadual Paulista
3 UNESP - Univ Estadual Paulista
4 UNESP - Univ Estadual Paulista
5 University of Campinas
6 University of Campinas
EditorFreitas, Carla Maria Dal Sasso
Sarkar, Sudeep
Scopigno, Roberto
Silva, Luciano
e-Mail Addresspapa.joaopaulo@gmail.com
Conference NameConference on Graphics, Patterns and Images, 25 (SIBGRAPI)
Conference LocationOuro Preto
DateAug. 22-25, 2012
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Tertiary TypeFull Paper
History2012-09-20 16:45:34 :: papa.joaopaulo@gmail.com -> administrator :: 2012
2020-02-19 02:18:28 :: administrator -> :: 2012
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Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
KeywordsImage classification, Evolutionary algorithms, Descriptor Combination.
AbstractThe efficiency in image classification tasks can be improved using combined information provided by several sources, such as shape, color, and texture visual properties. Although many works proposed to combine different feature vectors, we model the descriptor combination as an optimization problem to be addressed by evolutionary-based techniques, which compute distances between samples that maximize their separability in the feature space. The robustness of the proposed technique is assessed by the Optimum-Path Forest classifier. Experiments showed that the proposed methodology can outperform individual information provided by single descriptors in well-known public datasets.
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data URLhttp://urlib.net/rep/8JMKD3MGPBW34M/3C9U2C8
zipped data URLhttp://urlib.net/zip/8JMKD3MGPBW34M/3C9U2C8
Languageen
Target Fileopf-sibgrapi12-versao-final.pdf
User Grouppapa.joaopaulo@gmail.com
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Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
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Empty Fieldsaccessionnumber archivingpolicy archivist area callnumber copyholder copyright creatorhistory descriptionlevel dissemination documentstage doi edition electronicmailaddress group holdercode isbn issn label lineage mark nextedition nexthigherunit notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume

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