Advertisement

A Study on Various Dimensionality Reduction Techniques Applied in the General Shape Analysis

  • Katarzyna Gościewska
  • Dariusz FrejlichowskiEmail author
Chapter
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 342)

Abstract

The paper presents selected numerical data dimensionality reduction techniques and their application to reduce the size of feature vectors used in an exemplary General Shape Analysis (GSA) task. The usability of applying reduced feature vectors was experimentally tested using three Fourier transform-based shape descriptors and three data reduction approaches. The aim of the experiments was to investigate which data reduction approach is the best, i.e., gives the highest percentage effectiveness value while maintaining a minimal size of the feature vector.

Keywords

General shape analysis Fourier descriptors Feature dimensionality reduction 

References

  1. 1.
    Frejlichowski, D.: Analiza Ogólnego Ksztatu Obiektów Wydobytych z Obrazów Cyfrowych Rozpoznawanych z Użyciem Deskryptora PDH. Metody Informatyki Stosowanej 1, 5–13 (2009)Google Scholar
  2. 2.
    Frejlichowski, D.: The application of the Zernike moments to the problem of general shape analysis. Control Cybern. 40(2), 515–526 (2011)MathSciNetGoogle Scholar
  3. 3.
    Frejlichowski, D.: An experimental comparison of seven shape descriptors in the general shape analysis problem. In: Campilho, A., Kamel, M. (eds.) ICIAR 2010, Part I. LNCS, vol. 6111, pp. 294–305. Springer, Heidelberg (2010)Google Scholar
  4. 4.
    Rosin, P.L.: Measuring rectangularity. Mach. Vis. Appl. 11, 191–196 (1999)CrossRefGoogle Scholar
  5. 5.
    Rosin, P.L.: Measuring shape: ellipticity, rectangularity, and triangularity. Mach. Vis. Appl. 14, 172–184 (2003)CrossRefGoogle Scholar
  6. 6.
    Rosin, P.L.: Computing global shape measures. In: Chen, C.H., Wang, P.S.P. (eds.) Handbook of Pattern Recognition and Computer Vision, 3rd edn., pp. 177–196. World Scientific Publishing Company Inc. (2005)Google Scholar
  7. 7.
    Kukharev, G.: Digital Image Processing and Analysis. SUT Press, Stettin (1998)Google Scholar
  8. 8.
    Rauber, T.W.: Two-dimensional shape description. Technical report: GR UNINOVA-RT-10-94, Universidade Nova de Lisboa, Lisoba, Portugal (1994)Google Scholar
  9. 9.
    Zhang, D., Lu, G.: Shape-based image retrieval using generic Fourier descriptor. Signal Process.-Image 17(10), 825–848 (2002)CrossRefGoogle Scholar
  10. 10.
    Cunningham, P.: Dimension Reduction. Technical Report UCD-CSI-2007-7 (2007)Google Scholar
  11. 11.
    Orfanidis, S.J.: SVD, PCA, KLT, CCA, and all that. http://eceweb1.rutgers.edu/~orfanidi/ece525/svd.pdf
  12. 12.
    Fodor, I.K.: A Survey of Dimension Reduction Techniques, http://computation.llnl.gov/casc/sapphire/pubs/148494.pdf
  13. 13.
    Sahoolizadeh, H., Heidari, Z., Dehghani, H.: A new face recognition method using PCA, LDA and neural network. In: Proceedings of World Academy of Science, Engineering and Technology (2008)Google Scholar
  14. 14.
    Denkowski, M., Mikołajczak, P.: Przetwarzanie obrazów cyfrowych—laboratorium. Instytut Informatyki UMCS, Lublin (2011)Google Scholar
  15. 15.
    Frejlichowski, D., Gościewska, K.: Application of 2D Fourier descriptors and similarity measures to the general shape analysis problem. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds.) ICCVG 2012. LNCS, vol. 7594, pp. 371–378. Springer, Heidelberg (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Faculty of Computer Science and Information TechnologyWest Pomeranian University of TechnologySzczecinPoland

Personalised recommendations