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Automated Classification of Galaxy Images

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Knowledge-Based Intelligent Information and Engineering Systems (KES 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3215))


In this paper we present an experimental study of the performance of three machine learning algorithms applied to the difficult problem of galaxy classification. We use the Naive Bayes classifier, the rule-induction algorithm C4.5 and a recently introduced classifier named random forest (RF). We first employ image processing to standardize the images, eliminating the effects of orientation and scale, then perform principal component analysis to reduce the dimensionality of the data, and finally, classify the galaxy images. Our experiments show that RF obtains the best results considering three, five and seven galaxy types.

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© 2004 Springer-Verlag Berlin Heidelberg

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de la Calleja, J., Fuentes, O. (2004). Automated Classification of Galaxy Images. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2004. Lecture Notes in Computer Science(), vol 3215. Springer, Berlin, Heidelberg.

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23205-6

  • Online ISBN: 978-3-540-30134-9

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