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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3PK5D5L
Repositorysid.inpe.br/sibgrapi/2017/09.10.23.00
Last Update2017:09.10.23.00.26 (UTC) perhark@ime.usp.br
Metadata Repositorysid.inpe.br/sibgrapi/2017/09.10.23.00.26
Metadata Last Update2022:05.18.22.18.26 (UTC) administrator
Citation KeyMurrugarra-LLerenaHira:2017:GaImCl
TitleGalaxy image classification
FormatOn-line
Year2017
Access Date2024, May 02
Number of Files1
Size476 KiB
2. Context
Author1 Murrugarra-LLerena, Joseph Hans
2 Hirata, Nina Sumiko Tomita
Affiliation1 Institute of Mathematics and Statistics of the University of São Paulo, São Paulo, Brazil
2 Institute of Mathematics and Statistics of the University of São Paulo, São Paulo, Brazil
EditorTorchelsen, Rafael Piccin
Nascimento, Erickson Rangel do
Panozzo, Daniele
Liu, Zicheng
Farias, Mylène
Viera, Thales
Sacht, Leonardo
Ferreira, Nivan
Comba, João Luiz Dihl
Hirata, Nina
Schiavon Porto, Marcelo
Vital, Creto
Pagot, Christian Azambuja
Petronetto, Fabiano
Clua, Esteban
Cardeal, Flávio
e-Mail Addressperhark@ime.usp.br
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ, Brazil
Date17-20 Oct. 2017
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Book TitleProceedings
Tertiary TypeWork in Progress
History (UTC)2017-09-10 23:00:26 :: perhark@ime.usp.br -> administrator ::
2022-05-18 22:18:26 :: administrator -> :: 2017
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Keywordsgalaxy classification
deep learning
convolutional neural networks
AbstractOver the years, different methods based either on morphological features or on expert knowledge have been proposed to classify galaxies. The amount of data to be processed in large scale surveys poses a new challenge for the classification. In this preliminary study, we investigate machine learning methods for galaxy image classification. Specifically, we evaluate convolutional neural networks as tools to be used in the classification process. Different ways of using convolutional neural networks has been experimented to classify galaxies as elliptical or spiral. Classification accuracy around 90-91% for the Sloan Digital Sky Survey (SDSS) galaxy images has been achieved.
Arrangementurlib.net > SDLA > Fonds > SIBGRAPI 2017 > Galaxy image classification
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
agreement.html 10/09/2017 20:00 1.2 KiB 
4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3PK5D5L
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3PK5D5L
Languageen
Target Filearticle.pdf
User Groupperhark@ime.usp.br
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3PKCC58
Citing Item Listsid.inpe.br/sibgrapi/2017/09.12.13.04 7
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination doi 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 versiontype volume


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