Close

1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier6qtX3pFwXQZeBBx/GJPfJ
Repositorysid.inpe.br/banon/2005/07.12.19.51
Last Update2005:07.13.03.00.00 (UTC) administrator
Metadata Repositorysid.inpe.br/banon/2005/07.12.19.51.36
Metadata Last Update2022:06.14.00.12.58 (UTC) administrator
DOI10.1109/SIBGRAPI.2005.25
Citation KeyNeryCaPáQuMaCa:2005:DeApFe
TitleDetermining the appropriate feature set for fish classification tasks
FormatOn-line
Year2005
Access Date2024, Mar. 29
Number of Files1
Size255 KiB
2. Context
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, RN, Brazil
Date9-12 Oct. 2005
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2008-07-17 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 ::
2022-06-14 00:12:58 :: administrator -> :: 2005
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
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.
ArrangementFonds > Full Index > Determining the appropriate...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Contentthere are no files
4. Conditions of access and use
data URLhttp://urlib.net/ibi/6qtX3pFwXQZeBBx/GJPfJ
zipped data URLhttp://urlib.net/zip/6qtX3pFwXQZeBBx/GJPfJ
Languageen
Target Filepaduaf_fishclassification.pdf
User Groupcardeal
administrator
Visibilityshown
5. Allied materials
Next Higher Units8JMKD3MGPEW34M/46R3ED5
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2022/05.05.04.08 3
sid.inpe.br/banon/2001/03.30.15.38.24 1
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 mirrorrepository 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


Close