Parallel 2D Local Pattern Spectra of Invariant Moments for Galaxy Classification

  • Ugo Moschini
  • Paul Teeninga
  • Scott C. Trager
  • Michael H. F. Wilkinson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9257)


In this paper, we explore the possibility to use 2D pattern spectra as suitable feature vectors in galaxy classification tasks. The focus is on separating mergers from projected galaxies in a data set extracted from the Sloan Digital Sky Survey Data Release 7. Local pattern spectra are built in parallel and are based on an object segmentation obtained by filtering a max-tree structure that preserves faint structures. A set of pattern spectra using size and Hu’s and Flusser’s image invariant moments information is computed for every segmented galaxy. The C4.5 tree classifier with bagging gives the best classification result. Mergers and projected galaxies are classified with a precision of about 80%.


Classification Astronomy Pattern spectra Parallel computing 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Ugo Moschini
    • 1
  • Paul Teeninga
    • 1
  • Scott C. Trager
    • 2
  • Michael H. F. Wilkinson
    • 1
  1. 1.Johann Bernoulli InstituteUniversity of GroningenGroningenThe Netherlands
  2. 2.Kapteyn Astronomical InstituteUniversity of GroningenGroningenThe Netherlands

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