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 
             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 
  conference-location = "Niter{\'o}i, RJ",
      conference-year = "Oct. 17-20, 2017",
             language = "en",
                  ibi = "8JMKD3MGPAW/3PK5D5L",
                  url = "",
           targetfile = "article.pdf",
        urlaccessdate = "2021, Jan. 26"