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Statistical Classification of Audiovisual Data

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

Abstract

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.

Keywords

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.

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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

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