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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3M3PPPL
Repositorysid.inpe.br/sibgrapi/2016/07.11.14.00
Last Update2016:07.11.14.00.03 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2016/07.11.14.00.03
Metadata Last Update2022:06.14.00.08.21 (UTC) administrator
DOI10.1109/SIBGRAPI.2016.058
Citation KeyCamposMantJr:2016:MeApRe
TitleA Meta-learning Approach for Recommendation of Image Segmentation Algorithms
FormatOn-line
Year2016
Access Date2024, May 03
Number of Files1
Size11805 KiB
2. Context
Author1 Campos, Gabriel F. C.
2 Mantovani, Rafael G.
3 Jr., Sylvio Barbon
Affiliation1 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.
e-Mail Addressgabrielfcc@gmail.com
Conference NameConference on Graphics, Patterns and Images, 29 (SIBGRAPI)
Conference LocationSão José dos Campos, SP, Brazil
Date4-7 Oct. 2016
PublisherIEEE Computer Society´s Conference Publishing Services
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (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. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
KeywordsSegmentation algorithm recommendation
metalearning
image processing
AbstractThere 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.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2016 > A Meta-learning Approach...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > A Meta-learning Approach...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3M3PPPL
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3M3PPPL
Languageen
Target FilePID4348117.pdf
User Groupgabrielfcc@gmail.com
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3M2D4LP
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2016/07.02.23.50 5
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy 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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