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Improving the Recognition Performance of Moment Features by Selection

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Feature Selection for Data and Pattern Recognition

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

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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.

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References

  1. Belhumeur, P.N., Kriegman, D.J.: The Yale face database. http://cvc.yale.edu/projects/yalefaces/yalefaces.html (1997)

  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. Cipolla, R., Pentland, A.: Computer vision for human-machine interaction. Cambridge University Press, Cambridge (1998)

    Google Scholar 

  4. Flusser, J., Zitova, B., Suk, T.: Moments and Moment Invariants in Pattern Recognition. Wiley, Chichester (2009)

    Book  MATH  Google Scholar 

  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. Hosny, K.M.: Exact Legendre moment computation for gray level images. Pattern Recognit. 40(12), 3597–3605 (2007)

    Article  MATH  Google Scholar 

  7. Hosny, K.M.: Fast computation of accurate Zernike moments. J. Real-Time Image Process. 3(1–2), 97–107 (2008)

    Article  Google Scholar 

  8. Hosny, K.M.: Fast computation of accurate Gaussian-Hermite moments for image processing applications. Digit. Signal Process. 22(3), 476–485 (2012)

    Article  MathSciNet  Google Scholar 

  9. Hu, M.K.: Visual pattern recognition by moment invariants. IRE Trans. Inf. Theory 8(2), 179–187 (1962)

    Article  MATH  Google Scholar 

  10. Jain, A.K., Hong, L., Pankanti, S., Bolle, R.: An identity-authentication system using fingerprints. Proc. IEEE 85(9), 1365–1388 (1997)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. Kanan, H.R., Faez, K.: GA-based optimal selection of PZMI features for face recognition. Appl. Math. Comput. 205(2), 706–715 (2008)

    Article  MATH  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  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. 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. 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. Miezianko, R.: Terravic research infrared database. In: IEEE OTCBVS WS Series Bench. IEEE (2005)

    Google Scholar 

  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. 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. 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. 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. 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)

    Article  MathSciNet  MATH  Google Scholar 

  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. 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)

    Article  Google Scholar 

  26. Teague, M.R.: Image analysis via the general theory of moments. JOSA 70(8), 920–930 (1980)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  28. Velasin, S.A., Remagnino, P.: Intelligent Distributed Video Surveillance Systems, vol. 5. IET, London (2006)

    Book  Google Scholar 

  29. Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)

    Article  Google Scholar 

  30. Wayman, J., Jain, A., Maltoni, D., Maio, D.: An introduction to biometric authentication systems. Biometric Systems, pp. 1–20. Springer, London (2005)

    Chapter  Google Scholar 

  31. Wee, C.Y., Paramesran, R.: On the computational aspects of Zernike moments. Image Vis. Comput. 25(6), 967–980 (2007)

    Article  Google Scholar 

  32. Xin, Y., Pawlak, M., Liao, S.: Accurate computation of Zernike moments in polar coordinates. IEEE Trans. Image Process. 16(2), 581–587 (2007)

    Article  MathSciNet  Google Scholar 

  33. Yang, B., Dai, M.: Image analysis by Gaussian-Hermite moments. Signal Process. 91(10), 2290–2303 (2011)

    Article  MATH  Google Scholar 

  34. Yap, P.T., Paramesran, R., Ong, S.H.: Image analysis by Krawtchouk moments. IEEE Trans. Image Process. 12(11), 1367–1377 (2003)

    Article  MathSciNet  Google Scholar 

  35. Zernike, V.F.: Beugungstheorie des schneidenver-fahrens und seiner verbesserten form, der phasenkontrastmethode. Physica 1(7), 689–704 (1934)

    Article  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

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Authors and Affiliations

  1. Human Machines Interaction (HMI) Laboratory, Department of Computer and Informatics Engineering, Eastern Macedonia and Thrace (EMT) Institute of Technology, Ag. Loukas, 65404, Kavala, Greece

    George A. Papakostas

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  1. George A. Papakostas
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Correspondence to George A. Papakostas .

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Editors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Urszula Stańczyk

  2. Mawson Lakes Campus, Faculty of Education, Science, Technology and Mathematics, University of Canberra, Canberra, Australia, and University of South Australia, Adelaide, Australia

    Lakhmi C. Jain

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Papakostas, G.A. (2015). Improving the Recognition Performance of Moment Features by Selection. In: Stańczyk, U., Jain, L. (eds) Feature Selection for Data and Pattern Recognition. Studies in Computational Intelligence, vol 584. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45620-0_13

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  • DOI: https://doi.org/10.1007/978-3-662-45620-0_13

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  • Online ISBN: 978-3-662-45620-0

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