@InProceedings{Murrugarra-LLerenaHira:2017:GaImCl,
author = "Murrugarra-LLerena, Joseph Hans and Hirata, Nina Sumiko Tomita",
affiliation = "Institute of Mathematics and Statistics of the University of
S{\~a}o Paulo, S{\~a}o Paulo, Brazil and Institute of
Mathematics and Statistics of the University of S{\~a}o Paulo,
S{\~a}o Paulo, Brazil",
title = "Galaxy image classification",
booktitle = "Proceedings...",
year = "2017",
editor = "Torchelsen, Rafael Piccin and Nascimento, Erickson Rangel do and
Panozzo, Daniele and Liu, Zicheng and Farias, Myl{\`e}ne and
Viera, Thales and Sacht, Leonardo and Ferreira, Nivan and Comba,
Jo{\~a}o Luiz Dihl and Hirata, Nina and Schiavon Porto, Marcelo
and Vital, Creto and Pagot, Christian Azambuja and Petronetto,
Fabiano and Clua, Esteban and Cardeal, Fl{\'a}vio",
organization = "Conference on Graphics, Patterns and Images, 30. (SIBGRAPI)",
publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
address = "Porto Alegre",
keywords = "galaxy classification, deep learning, convolutional neural
networks.",
abstract = "Over 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.",
conference-location = "Niter{\'o}i, RJ, Brazil",
conference-year = "17-20 Oct. 2017",
language = "en",
ibi = "8JMKD3MGPAW/3PK5D5L",
url = "http://urlib.net/ibi/8JMKD3MGPAW/3PK5D5L",
targetfile = "article.pdf",
urlaccessdate = "2024, May 01"
}