Statistical Classification of Audiovisual Data

  • Andrey V. Savchenko
Part of the SpringerBriefs in Optimization book series (BRIEFSOPTI)


In this chapter we explore a mathematical model for representation of audiovisual data as a sequence of independent segments. Each segment is associated with a sample of independent identically distributed primitive features. Based on this model the classification task is reduced to a problem of complex hypothesis testing of segment homogeneity. According to this approach, several nearest neighbor criteria are implemented. The well-known special cases are emphasized for some of them, e.g., the probabilistic neural network and the minimum Jensen–Shannon divergence principle. An experimental study in the face recognition problem is presented. It is shown that the segment homogeneity testing improves the accuracy when compared with the contemporary classification methods.


Face Recognition Facial Image Local Binary Pattern Exponential Family Dissimilarity Measure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. [1]
    Borovkov, A.A.: Mathematical Statistics. Gordon and Breach Science Publishers, Amsterdam (1998)zbMATHGoogle Scholar
  2. [2]
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 886–893 (2005)Google Scholar
  3. [3]
    Denison, D.G.: Bayesian Methods for Nonlinear Classification and Regression. Wiley Series in Probability and Statistics, vol. 386. Wiley, New York (2002)Google Scholar
  4. [4]
    Haykin, S.O.: Neural Networks and Learning Machines, 3rd edn. Prentice Hall, Harlow (2008)Google Scholar
  5. [5]
    Huang, G.B., Mattar, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments. In: Workshop on Faces in ’Real-Life’ Images: Detection, Alignment, and Recognition (2008)Google Scholar
  6. [6]
    Jenssen, R., Erdogmus, D., Principe, J., Eltoft, T.: Some equivalences between kernel methods and information theoretic methods. J. VLSI Sig. Proc. 45, 49–65 (2006)CrossRefzbMATHGoogle Scholar
  7. [7]
    Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the ACM International Conference on Multimedia, pp. 675–678. ACM (2014)Google Scholar
  8. [8]
    Kullback, S.: Information Theory and Statistics. Dover Publications, New York (1997)zbMATHGoogle Scholar
  9. [9]
    Lehmann, E.L., Romano, J.P.: Testing Statistical Hypotheses, 3rd edn. Springer, New York (2008)zbMATHGoogle Scholar
  10. [10]
    Li, S.Z., Jain, A.K. (eds.): Handbook of Face Recognition, 2nd edn. Springer, London/ New York (2011)Google Scholar
  11. [11]
    Liao, S., Zhu, X., Lei, Z., Zhang, L., Li, S.Z.: Learning multi-scale block local binary patterns for face recognition. In: Lee, S.W., Li, S.Z. (eds.) Proceedings of the International Conference on Advances in Biometrics (ICB), vol. 4642, pp. 828–837. Springer (2007)Google Scholar
  12. [12]
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRefGoogle Scholar
  13. [13]
    Martins, A.F.T., Figueiredo, M.A.T., Aguiar, P.M.Q., Smith, N.A., Xing, E.P.: Nonextensive entropic kernels. In: International Conference on Machine Learning, pp. 640–647. ACM (2008)Google Scholar
  14. [14]
    Ortiz, E.G., Becker, B.C.: Face recognition for web-scale datasets. Comput. Vis. Image Underst. 118, 153–170 (2014)CrossRefGoogle Scholar
  15. [15]
    Parkhi, O.M., Vedaldi, A., Zisserman, A.: Deep face recognition. In: British Machine Vision Conference (2015)CrossRefGoogle Scholar
  16. [16]
    Ruiz-del Solar, J., Navarrete, P.: Eigenspace-based face recognition: a comparative study of different approaches. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 35(3), 315–325 (2005)CrossRefzbMATHGoogle Scholar
  17. [17]
    Rutkowski, L.: Computational Intelligence: Methods and Techniques. Springer, Heidelberg (2010)zbMATHGoogle Scholar
  18. [18]
    Savchenko, A.V.: Statistical recognition of a set of patterns using novel probability neural network. In: Mana, N., Schwenker, F., Trentin, E. (eds.) Proceedings of the International Workshop on Artificial Neural Networks in Pattern Recognition (ANNPR). Lecture Notes in Computer Science, vol. 7477, pp. 93–103. Springer-Verlag Berlin Heidelberg (2012)CrossRefGoogle Scholar
  19. [19]
    Savchenko, A.V.: Probabilistic neural network with homogeneity testing in recognition of discrete patterns set. Neural Netw. 46, 227–241 (2013)CrossRefzbMATHGoogle Scholar
  20. [20]
    Savchenko, A.V.: Nonlinear transformation of the distance function in the nearest neighbor image recognition. In: Zhang, Y.J., Tavares, J.M.R.S. (eds.) Proceedings of the International Conference on Computational Modeling of Objects Presented in Images (CompIMAGE), Lecture Notes in Computer Science, vol. 8641, pp. 261–266. Springer International Publishing Switzerland (2014)Google Scholar
  21. [21]
    Savchenko, A.V.: Fast multi-class recognition of piecewise regular objects based on sequential three-way decisions and granular computing. Knowl.-Based Syst. 91, 252–262 (2016)CrossRefGoogle Scholar
  22. [22]
    Savchenko, A.V., Belova, N.S.: Statistical testing of segment homogeneity in classification of piecewise-regular objects. Int. J. Appl. Math. Comput. Sci. 25(4), 915–925 (2015)CrossRefGoogle Scholar
  23. [23]
    Shapiro, L.G., Stockman, G.C.: Computer Vision. Prentice Hall, Upper Saddle River (2001)Google Scholar
  24. [24]
    Specht, D.F.: Probabilistic neural networks. Neural Netw. 3(1), 109–118 (1990)CrossRefGoogle Scholar
  25. [25]
    Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 4th edn. Academic, Burlington/London (2008)zbMATHGoogle Scholar
  26. [26]
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 511–518 (2001)Google Scholar
  27. [27]
    Zhou, E., Cao, Z., Yin, Q.: Naive-deep face recognition: touching the limit of LFW benchmark or not? CoRR (2015). abs/1501.04690Google Scholar

Copyright information

© The Author(s) 2016

Authors and Affiliations

  • Andrey V. Savchenko
    • 1
  1. 1.Laboratory of Algorithms and Technologies for Network AnalysisNational Research University Higher School of EconomicsNizhny NovgorodRussia

Personalised recommendations