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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3M3C9G8
Repositorysid.inpe.br/sibgrapi/2016/07.08.22.58
Last Update2016:07.08.22.58.58 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2016/07.08.22.58.58
Metadata Last Update2022:06.14.00.08.18 (UTC) administrator
DOI10.1109/SIBGRAPI.2016.062
Citation KeyAfonsoVidKurFalPap:2016:LeClSe
TitleLearning to Classify Seismic Images with Deep Optimum-Path Forest
FormatOn-line
Year2016
Access Date2024, May 03
Number of Files1
Size754 KiB
2. Context
Author1 Afonso, Luis Claudio Sugi
2 Vidal, Alexandre Campane
3 Kuroda, Michelle Chaves
4 Falcao, Alexandre Xavier
5 Papa, Joao Paulo
Affiliation1 Federal University of Sao Carlos
2 University of Campinas
3 University of Campinas
4 University of Campinas
5 Sao Paulo State University
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 Addresspapa.joaopaulo@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)2016-07-08 22:58:58 :: papa.joaopaulo@gmail.com -> administrator ::
2016-10-05 14:49:09 :: administrator -> papa.joaopaulo@gmail.com :: 2016
2016-10-13 17:38:03 :: papa.joaopaulo@gmail.com -> administrator :: 2016
2022-06-14 00:08:18 :: administrator -> :: 2016
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
KeywordsOptimum-Path Forest
Image Clustering
Deep Representations
Seismic Images
AbstractDue to the lack of labeled information, clustering techniques have been paramount in the last years once more. In this paper, inspired by the deep learning phenomenon, we presented a multi-scale approach to obtain more refined cluster representations of the Optimum-Path Forest (OPF) classifier, which has obtained promising results in a number of works in the literature. Here, we propose to fill a gap in OPF-based works by using a deep-driven representation of the feature space. Additionally, we validated the work in the context of high resolution seismic images aiming at petroleum exploration, as well as in general-purpose applications. Quantitative and qualitative analysis are conducted in order to assess the robustness of the proposed approach.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2016 > Learning to Classify...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Learning to Classify...
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/3M3C9G8
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3M3C9G8
Languageen
Target Filepaper.pdf
User Grouppapa.joaopaulo@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 9
sid.inpe.br/sibgrapi/2022/06.10.21.49 1
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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