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Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier6qtX3pFwXQZeBBx/GJPfJ
Repositorysid.inpe.br/banon/2005/07.12.19.51
Last Update2005:07.13.03.00.00 administrator
Metadatasid.inpe.br/banon/2005/07.12.19.51.36
Metadata Last Update2020:02.19.03.19.13 administrator
Citation KeyNeryCaPáQuMaCa:2005:DeApFe
TitleDetermining the appropriate feature set for fish classification tasks
FormatOn-line
Year2005
Date9-12 Oct. 2005
Access Date2021, Jan. 19
Number of Files1
Size255 KiB
Context area
Author1 Nery, Marcelo Souza
2 Campos, Mario Fernando Montenegro
3 Pádua, Flávio Luis Cardeal
4 Queiroz Neto, José Pinheiro de
5 Machado, Alexei Manso Correa
6 Carceroni, Rodrigo Lima
Affiliation1 Departamento de Ciência da Computação - Universidade Federal de Minas Gerais
2 Pontifícia Universidade Católica de Minas Gerais
3 Centro Federal de Educação Tecnológica do Amazonas
EditorRodrigues, Maria Andréia Formico
Frery, Alejandro César
e-Mail Addresscardeal@dcc.ufmg.br
Conference NameBrazilian Symposium on Computer Graphics and Image Processing, 18 (SIBGRAPI)
Conference LocationNatal
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Tertiary TypeFull Paper
History2008-07-17 14:11:00 :: cardeal -> banon ::
2008-08-26 15:17:02 :: banon -> administrator ::
2009-08-13 20:37:48 :: administrator -> banon ::
2010-08-28 20:01:18 :: banon -> administrator ::
2020-02-19 03:19:13 :: administrator -> :: 2005
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Keywordsobject classification, feature extraction, feature selection, fish classification.
AbstractWe present a novel fish classification methodology based on a robust feature selection technique. Unlike existing works for fish classification, which propose descriptors and do not analyze their individual impacts in the whole classification task, we propose a general set of features and their correspondent weights that should be used as a priori information by the classifier. In this sense, instead of studying techniques for improving the classifiers structure itself, we consider it as a "black box" and focus our research in the determination of which input information must bring a robust fish discrimination. All the experiments were performed with fish species of Rio Grande river in Minas Gerais, Brazil. This work has been developed as part of a wider research, which has as main goal the development of effective fish ladders for the Brazilian dams.
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data URLhttp://urlib.net/rep/6qtX3pFwXQZeBBx/GJPfJ
zipped data URLhttp://urlib.net/zip/6qtX3pFwXQZeBBx/GJPfJ
Languageen
Target Filepaduaf_fishclassification.pdf
User Groupcardeal
administrator
Visibilityshown
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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 mirrorrepository 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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