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Reference TypeConference Paper (Conference Proceedings)
Last Update2017:
Metadata Last Update2020: administrator
Citation KeyMurrugarra-LLerenaHira:2017:GaImCl
TitleGalaxy image classification
DateOct. 17-20, 2017
Access Date2021, Jan. 21
Number of Files1
Size476 KiB
Context area
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
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ
Book TitleProceedings
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Tertiary TypeWork in Progress
History2017-09-10 23:00:26 :: -> administrator ::
2020-02-20 22:06:48 :: administrator -> :: 2017
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Is the master or a copy?is the master
Content Stagecompleted
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.
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