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Automated classification of galaxies using invariant moments


Elfattah, MA and Abu Elsoud, MA and Hassanien, AE and Kim, TH, Automated classification of galaxies using invariant moments, Proceedings of the 4th International Conference on Future Generation Information Technology, 16-19 December 2012, Gangneung, Kangwondo, Korea, pp. 103-111. ISBN 978-3-642-35584-4 (2012) [Refereed Conference Paper]

Copyright Statement

Copyright 2012 Springer-Verlag Berlin Heidelberg

DOI: doi:10.1007/978-3-642-35585-1_14


Classification and identification of galaxy shape is an important issue for astronauts since it provides valuable information about the origin and the evolution of the universe. Statistical invariant features that are functions of moments have been used as global features of galaxy images in their pattern recognition. In this paper, an automated training based recognition system that can compute the statistical invariant features for different galaxy shapes is investigated. The proposed algorithm is robust, regardless of orientation, size and position of the galaxy inside the image. Feature vectors are computed via nonlinear moment invariant functions for each galaxy shape. After feature extraction, the recognition performance of classifier in conjunction with these moment–based features is introduced. Computer simulations show that Galaxy images are classified with an accuracy of about 90% compared to the human visual classification system.

Item Details

Item Type:Refereed Conference Paper
Keywords:invariant moments, mean squared error ( MSE), Fisher score
Research Division:Mathematical Sciences
Research Group:Mathematical physics
Research Field:Mathematical physics not elsewhere classified
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in the mathematical sciences
UTAS Author:Kim, TH (Dr Tai Kim)
ID Code:85064
Year Published:2012
Deposited By:Research Division
Deposited On:2013-06-12
Last Modified:2014-08-06

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