Improving the Recognition Performance of Moment Features by Selection

Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 584)

Abstract

This chapter deals with the selection of the most appropriate moment features used to recognize known patterns. This chapter aims to highlight the need for selection of moment features subject to their descriptive capabilities. For this purpose, some popular moment families are presented and their properties, making them suitable for pattern recognition tasks, are discussed. Two different types of feature selection algorithms, a simple Genetic Algorithm (GA) and the Relief algorithm are applied to select the moment features that better discriminate human faces and facial expressions, under several pose and illumination conditions. Appropriate experiments using four benchmark datasets have been conducted in order to investigate the theoretical assertions. An extensive experimental analysis has shown that the recognition performance of the moment features can be significantly improved by selecting them from a predefined pool, relative to a specific application.

Keywords

Moment descriptors Pattern recognition Feature selection Genetic algorithms Relief algorithm 

References

  1. 1.
    Belhumeur, P.N., Kriegman, D.J.: The Yale face database. http://cvc.yale.edu/projects/yalefaces/yalefaces.html (1997)
  2. 2.
    Chen, B.J., Shu, H.Z., Zhang, H., Chen, G., Toumoulin, C., Dillenseger, J.L., Luo, L.M.: Quaternion Zernike moments and their invariants for color image analysis and object recognition. Signal Process. 92(2), 308–318 (2012)Google Scholar
  3. 3.
    Cipolla, R., Pentland, A.: Computer vision for human-machine interaction. Cambridge University Press, Cambridge (1998)Google Scholar
  4. 4.
    Flusser, J., Zitova, B., Suk, T.: Moments and Moment Invariants in Pattern Recognition. Wiley, Chichester (2009)CrossRefMATHGoogle Scholar
  5. 5.
    Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. U Michigan Press, Ann Arbor (1975)Google Scholar
  6. 6.
    Hosny, K.M.: Exact Legendre moment computation for gray level images. Pattern Recognit. 40(12), 3597–3605 (2007)CrossRefMATHGoogle Scholar
  7. 7.
    Hosny, K.M.: Fast computation of accurate Zernike moments. J. Real-Time Image Process. 3(1–2), 97–107 (2008)CrossRefGoogle Scholar
  8. 8.
    Hosny, K.M.: Fast computation of accurate Gaussian-Hermite moments for image processing applications. Digit. Signal Process. 22(3), 476–485 (2012)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Hu, M.K.: Visual pattern recognition by moment invariants. IRE Trans. Inf. Theory 8(2), 179–187 (1962)CrossRefMATHGoogle Scholar
  10. 10.
    Jain, A.K., Hong, L., Pankanti, S., Bolle, R.: An identity-authentication system using fingerprints. Proc. IEEE 85(9), 1365–1388 (1997)CrossRefGoogle Scholar
  11. 11.
    Kaburlasos, V.G., Papadakis, S.E., Papakostas, G.A.: Lattice computing extension of the FAM neural classifier for human facial expression recognition. IEEE Trans. Neural Netw. Learn. Syst. 24(10), 1526–1538 (2013)CrossRefGoogle Scholar
  12. 12.
    Kadir, A., Nugroho, L.E., Santosa, P.I.: Experiments of Zernike moments for leaf identification. J. Theor. Appl. Inf. Technol. 41(1), 82–93 (2012)Google Scholar
  13. 13.
    Kanan, H.R., Faez, K.: GA-based optimal selection of PZMI features for face recognition. Appl. Math. Comput. 205(2), 706–715 (2008)CrossRefMATHGoogle Scholar
  14. 14.
    Karakasis, E.G., Papakostas, G.A., Koulouriotis, D.E., Tourassis, V.D.: Generalized dual Hahn moment invariants. Pattern Recognit. 46(7), 1998–2014 (2013)CrossRefMATHGoogle Scholar
  15. 15.
    Kira, K., Rendell, L.A.: A practical approach to feature selection. In: Proceedings of the 9th International Workshop on Machine Learning, pp. 249–256. Morgan Kaufmann Publishers Inc. (1992)Google Scholar
  16. 16.
    Langner, O., Dotsch, R., Bijlstra, G., Wigboldus, D.H., Hawk, S.T., van Knippenberg, A.: Presentation and validation of the Radboud faces database. Cogn. Emot. 24(8), 1377–1388 (2010)Google Scholar
  17. 17.
    Lyons, M., Akamatsu, S., Kamachi, M., Gyoba, J.: Coding facial expressions with Gabor wavelets. In: Proceedings of the 3rd IEEE International Conference on Automatic Face and Gesture Recognition, pp. 200–205. IEEE (1998)Google Scholar
  18. 18.
    Miezianko, R.: Terravic research infrared database. In: IEEE OTCBVS WS Series Bench. IEEE (2005)Google Scholar
  19. 19.
    Mukundan, R., Ong, S.H., Lee, P.A.: Image analysis by Tchebichef moments. IEEE Trans. Image Process. 10(9), 1357–1364 (2001)Google Scholar
  20. 20.
    Papakostas, G.A., Boutalis, Y.S., Mertzios, B.G.: Evolutionary selection of Zernike moment sets in image processing. In: Proceedings of the 10th International Workshop on Systems, Signals and Image Processing (IWSSIP’03), pp. 10–11 (2003)Google Scholar
  21. 21.
    Papakostas, G.A., Karakasis, E.G., Koulouriotis, D.E.: Orthogonal image moment invariants: highly discriminative features for pattern recognition applications. In: Cross-Disciplinary Applications of Artificial Intelligence and Pattern Recognition: Advancing Technologies, pp. 34–52. IGI Global (2012)Google Scholar
  22. 22.
    Papakostas, G.A., Kaburlasos, V.G., Pachidis, T.: Thermal infrared face recognition based on lattice computing (LC) techniques. In: 2013 IEEE International Conference on Fuzzy Systems (FUZZ), pp. 1–6. IEEE (2013)Google Scholar
  23. 23.
    Papakostas, G.A., Boutalis, Y.S., Karras, D.A., Mertzios, B.G.: A new class of Zernike moments for computer vision applications. Inf. Sci. 177(13), 2802–2819 (2007)CrossRefMathSciNetMATHGoogle Scholar
  24. 24.
    Papakostas, G.A., Koulouriotis, D.E., Polydoros, A.S., Tourassis, V.D.: Evolutionary feature subset selection for pattern recognition applications. In: Evolutionary Algorithms, pp. 443–458. InTech (2011)Google Scholar
  25. 25.
    Papakostas, G.A., Koulouriotis, D.E., Karakasis, E.G., Tourassis, V.D.: Moment-based local binary patterns: a novel descriptor for invariant pattern recognition applications. Neurocomputing 99(1), 358–371 (2013)CrossRefGoogle Scholar
  26. 26.
    Teague, M.R.: Image analysis via the general theory of moments. JOSA 70(8), 920–930 (1980)CrossRefMathSciNetGoogle Scholar
  27. 27.
    Tsougenis, E.D., Papakostas, G.A., Koulouriotis, D.E., Tourassis, V.D.: Performance evaluation of moment-based watermarking methods: a review. J. Syst. Softw. 85(8), 1864–1884 (2012)CrossRefGoogle Scholar
  28. 28.
    Velasin, S.A., Remagnino, P.: Intelligent Distributed Video Surveillance Systems, vol. 5. IET, London (2006)CrossRefGoogle Scholar
  29. 29.
    Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)CrossRefGoogle Scholar
  30. 30.
    Wayman, J., Jain, A., Maltoni, D., Maio, D.: An introduction to biometric authentication systems. Biometric Systems, pp. 1–20. Springer, London (2005)CrossRefGoogle Scholar
  31. 31.
    Wee, C.Y., Paramesran, R.: On the computational aspects of Zernike moments. Image Vis. Comput. 25(6), 967–980 (2007)CrossRefGoogle Scholar
  32. 32.
    Xin, Y., Pawlak, M., Liao, S.: Accurate computation of Zernike moments in polar coordinates. IEEE Trans. Image Process. 16(2), 581–587 (2007)CrossRefMathSciNetGoogle Scholar
  33. 33.
    Yang, B., Dai, M.: Image analysis by Gaussian-Hermite moments. Signal Process. 91(10), 2290–2303 (2011)CrossRefMATHGoogle Scholar
  34. 34.
    Yap, P.T., Paramesran, R., Ong, S.H.: Image analysis by Krawtchouk moments. IEEE Trans. Image Process. 12(11), 1367–1377 (2003)CrossRefMathSciNetGoogle Scholar
  35. 35.
    Zernike, V.F.: Beugungstheorie des schneidenver-fahrens und seiner verbesserten form, der phasenkontrastmethode. Physica 1(7), 689–704 (1934)CrossRefMATHGoogle Scholar
  36. 36.
    Zhang, F., Liu, S.Q., Wang, D.B., Guan, W.: Aircraft recognition in infrared image using wavelet moment invariants. Image Vis. Comput. 27(4), 313–318 (2009)CrossRefGoogle Scholar
  37. 37.
    Zhu, H., Shu, H., Zhou, J., Luo, L., Coatrieux, J.L.: Image analysis by discrete orthogonal dual Hahn moments. Pattern Recognit. Lett. 28(13), 1688–1704 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Human Machines Interaction (HMI) Laboratory, Department of Computer and Informatics EngineeringEastern Macedonia and Thrace (EMT) Institute of TechnologyAg. LoukasGreece

Personalised recommendations