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1. Identificação
Tipo de ReferênciaArtigo em Evento (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Código do Detentoribi 8JMKD3MGPEW34M/46T9EHH
Identificador8JMKD3MGPAW/3M3PPPL
Repositóriosid.inpe.br/sibgrapi/2016/07.11.14.00
Última Atualização2016:07.11.14.00.03 (UTC) administrator
Repositório de Metadadossid.inpe.br/sibgrapi/2016/07.11.14.00.03
Última Atualização dos Metadados2022:06.14.00.08.21 (UTC) administrator
DOI10.1109/SIBGRAPI.2016.058
Chave de CitaçãoCamposMantJr:2016:MeApRe
TítuloA Meta-learning Approach for Recommendation of Image Segmentation Algorithms
FormatoOn-line
Ano2016
Data de Acesso17 set. 2024
Número de Arquivos1
Tamanho11805 KiB
2. Contextualização
Autor1 Campos, Gabriel F. C.
2 Mantovani, Rafael G.
3 Jr., Sylvio Barbon
Afiliação1 Londrina State University (UEL)
2 University of Sao Paulo (USP)
3 Londrina State University (UEL)
EditorAliaga, Daniel G.
Davis, Larry S.
Farias, Ricardo C.
Fernandes, Leandro A. F.
Gibson, Stuart J.
Giraldi, Gilson A.
Gois, João Paulo
Maciel, Anderson
Menotti, David
Miranda, Paulo A. V.
Musse, Soraia
Namikawa, Laercio
Pamplona, Mauricio
Papa, João Paulo
Santos, Jefersson dos
Schwartz, William Robson
Thomaz, Carlos E.
Endereço de e-Mailgabrielfcc@gmail.com
Nome do EventoConference on Graphics, Patterns and Images, 29 (SIBGRAPI)
Localização do EventoSão José dos Campos, SP, Brazil
Data4-7 Oct. 2016
Editora (Publisher)IEEE Computer Society´s Conference Publishing Services
Cidade da EditoraLos Alamitos
Título do LivroProceedings
Tipo TerciárioFull Paper
Histórico (UTC)2016-07-11 14:00:03 :: gabrielfcc@gmail.com -> administrator ::
2016-10-05 14:49:10 :: administrator -> gabrielfcc@gmail.com :: 2016
2016-10-13 03:29:44 :: gabrielfcc@gmail.com -> administrator :: 2016
2022-06-14 00:08:21 :: administrator -> :: 2016
3. Conteúdo e estrutura
É a matriz ou uma cópia?é a matriz
Estágio do Conteúdoconcluido
Transferível1
Tipo de Versãofinaldraft
Palavras-ChaveSegmentation algorithm recommendation
metalearning
image processing
ResumoThere are many algorithms for image segmentation, but there is no optimal algorithm for all kind of image applications. To recommend an adequate algorithm for segmentation is a challenging task that requires knowledge about the problem and algorithms. Meta-learning has recently emerged from machine learning research field to solve the algorithm selection problem. This paper applies meta-learning to recommend segmentation algorithms based on meta-knowledge. We performed experiments in four different meta-databases representing various real world problems, recommending when three different segmentation techniques are adequate or not. A set of 44 features based on color, frequency domain, histogram, texture, contrast and image quality were extracted from images, obtaining enough discriminative power for the recommending task in different segmentation scenarios. Results show that Random Forest meta-models were able to recommend segmentation algorithms with high predictive performance.
Arranjo 1urlib.net > SDLA > Fonds > SIBGRAPI 2016 > A Meta-learning Approach...
Arranjo 2urlib.net > SDLA > Fonds > Full Index > A Meta-learning Approach...
Conteúdo da Pasta docacessar
Conteúdo da Pasta sourcenão têm arquivos
Conteúdo da Pasta agreement
agreement.html 11/07/2016 11:00 1.2 KiB 
4. Condições de acesso e uso
URL dos dadoshttp://urlib.net/ibi/8JMKD3MGPAW/3M3PPPL
URL dos dados zipadoshttp://urlib.net/zip/8JMKD3MGPAW/3M3PPPL
Idiomaen
Arquivo AlvoPID4348117.pdf
Grupo de Usuáriosgabrielfcc@gmail.com
Visibilidadeshown
Permissão de Atualizaçãonão transferida
5. Fontes relacionadas
Repositório Espelhosid.inpe.br/banon/2001/03.30.15.38.24
Unidades Imediatamente Superiores8JMKD3MGPAW/3M2D4LP
8JMKD3MGPEW34M/4742MCS
Lista de Itens Citandosid.inpe.br/sibgrapi/2016/07.02.23.50 22
sid.inpe.br/sibgrapi/2022/06.10.21.49 5
Acervo Hospedeirosid.inpe.br/banon/2001/03.30.15.38
6. Notas
Campos Vaziosarchivingpolicy 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


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