Principal Component Analysis Neural Network Hybrid Classification Approach for Galaxies Images

  • Mohamed Abd. Elfattah
  • Nashwa El-Bendary
  • Mohamed A. Abou Elsoud
  • Jan Platoš
  • Aboul Ella Hassanien
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 237)


This article presents an automatic hybrid approach for galaxies images classification based on principal component analysis (PCA) neural network and moment-based features extraction algorithms. The proposed approach is consisted of four phases; namely image denoising, feature extraction, reduct generation, and classification phases. For the denoising phase, noise pixels are removed from input images, then input galaxy image is normalized to a uniform scale and Hu seven invariant moment algorithm is applied to reduce the dimensionality of the feature space during the feature extraction phase. Subsequently, for reduct generation phase, attributes in the information system table that is more important to the knowledge is generated as a subset of attributes. Rough set is used as feature reduction approach. The subset of attributed, which is called a reduct, is fully characterizing the knowledge in the database. Finally, during the classification phase, principal component analysis neural network algorithm is utilized for classifying the input galaxies images into one of four obtained source catalogue types. Experimental results showed that combining PCA and rough set as feature reduction techniques along with invariant moments for feature extraction provided better classification results than having no rough set feature reduction technique applied. It is also concluded that a small set of features is sufficient to classify galaxy images and provide a fast classification.


PCA neural network rough set moment invariant fisher score galaxy images 


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  1. 1.
    de la Calleja, J., Fuentes, O.: Automated Classification of Galaxy Images. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds.) KES 2004. LNCS (LNAI), vol. 3215, pp. 411–418. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  2. 2.
    Lahav, O.: Artificial neural networks as a tool for galaxy classification. In: Data Analysis in Astronomy, Erice, Italy (1996)Google Scholar
  3. 3.
    Pettini, M., Christensen, L., D’Odorico, S., Belokurov, V., Evans, N.W., Hewett, P.C., Koposov, S., Mason, E., Vernet, J.: CASSOWARY20: a wide separation Einstein Cross identified with the X-shooter spectrograph. Monthly Notices of the Royal Astronomical Society 402, 2335–2343 (2010)CrossRefGoogle Scholar
  4. 4.
    Frei, Z.: Zsolt Frei Galaxy Catalog, Princeton University, Department of Astrophysical Sciences (1999), (retrieved 2002)
  5. 5.
    De La Calleja, J., Fuentes, O.: Machine Learning and Image Analysis for Morphological Galaxy Classifcation. Monthly Notices of Royal Astronomical Society 349(1), 87–93 (2004)CrossRefGoogle Scholar
  6. 6.
    Mohamed, M.A., Atta, M.M.: Automated Classification of Galaxies Using Transformed Domain Features. IJCSNS International Journal of Computer Science and Network Security 10(2) (2010)Google Scholar
  7. 7.
    Hu, M.: Visual pattern recognition by moment invariants. IEEE Transactions on Information Theory 8, 179–187 (1962)MATHGoogle Scholar
  8. 8.
    Duda, Stork, D.G.: Pattern Classification. Wiley-Interscience Publication (2001)Google Scholar
  9. 9.
    Hsieh, W.W.: Nonlinear Principal Component Analysis by Neural Network. Univeristy of British Columbia, Appeared in Tellus 53A, 559–615 (2001)Google Scholar
  10. 10.
    Jahanbani, A., Karimi, H.: A new Approach for Detecting Intrusions Based on the PCA Neural Networks. J. Basic. Appl. Sci. Res. 2(1), 672–679 (2012)Google Scholar
  11. 11.
    Pawlak, Z.: Rough sets. International J. Comp. Inform. Science 11, 341–356 (1982)MathSciNetCrossRefMATHGoogle Scholar
  12. 12.
    Pawlak, Z.: Rough sets – Theoretical aspects of reasoning about data. Kluwer (1991)Google Scholar
  13. 13.
    Pawlak, Z., Grzymala-Busse, J., Slowinski, R., Ziarko, W.: Rough Sets. Communications of the ACM 38(11), 88–95 (1995)CrossRefGoogle Scholar
  14. 14.
    Polkowski, L.: Rough Sets: Mathematical Foundations. Physica-Verlag (2003)Google Scholar
  15. 15.
    Frei, Z.: Zsolt Frei Galaxy Catalog, Princeton University, Department of Astrophysical Sciences (1999), (retrieved 2002)
  16. 16.
    Banerji, M., Lahav, O., Lintott, C.J., Abdala, F., Schawinski, K., Bamford, S., Andreescu, D., Raddick, M., Murray, P., Jordan, M., Slosar, A., Szalay, A., Thomas, D., Vandenberg, J.: Galaxy Zoo: reproducing galaxy morphologies via machine learning. Monthly Notices of the Royal Astronomical Society 406, 342–353 (2010)CrossRefGoogle Scholar
  17. 17.
    Bishop, D.V.M., North, T., Donlan, C.: Nonword repetition as a behavioural marker for inherited language impairment: Evidence from a twin study. Journal of Child Psychology and Psychiatry 37, 391–403 (1996)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Mohamed Abd. Elfattah
    • 1
    • 2
  • Nashwa El-Bendary
    • 3
    • 2
  • Mohamed A. Abou Elsoud
    • 1
  • Jan Platoš
    • 4
  • Aboul Ella Hassanien
    • 5
    • 2
  1. 1.Faculty of Computers and InformationMansoura UniversityMansouraEgypt
  2. 2.Scientific Research Group in Egypt (SRGE)GizaEgypt
  3. 3.Arab Academy for Science, Technology, and Maritime TransportCairoEgypt
  4. 4.Department of Computer Science, FEECS and IT4 InnovationsVSB-Technical University of OstravaOstravaCzech Republic
  5. 5.Faculty of Computers and InformationCairo UniversityCairoEgypt

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