International Conference on Analysis of Images, Social Networks and Texts

Analysis of Images, Social Networks and Texts pp 14-23

Sequential Hierarchical Image Recognition Based on the Pyramid Histograms of Oriented Gradients with Small Samples

  • Andrey V. Savchenko
  • Vladimir R. Milov
  • Natalya S. Belova
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 542)

Abstract

In this paper we explore an application of the pyramid HOG (Histograms of Oriented Gradients) features in image recognition problem with small samples. A sequential analysis is used to improve the performance of hierarchical methods. We propose to process the next, more detailed level of pyramid only if the decision at the current level is unreliable. The Chow’s reject option of comparison of the posterior probability with a fixed threshold is used to verify recognition reliability. The posterior probability is estimated for the homogeneity-testing probabilistic neural network classifier on the basis of its relation with the Bayesian decision. Experimental results in face recognition are presented. It is shown that the proposed approach allows to increase the recognition performance in 2–4 times in comparison with conventional classification of pyramid HOGs.

Keywords

Image recognition Hierarchical recognition Sequential analysis Chow’s reject option Probabilistic neural network HOG (Histograms of oriented Gradients) PHOG (Pyramid HOG) 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Andrey V. Savchenko
    • 1
  • Vladimir R. Milov
    • 2
  • Natalya S. Belova
    • 3
  1. 1.Laboratory of Algorithms and Technologies for Network AnalysisNational Research University Higher School of EconomicsNizhny NovgorodRussia
  2. 2.Nizhny Novgorod State Technical University n.a. R.E. AlekseevNizhny NovgorodRussia
  3. 3.National Research University Higher School of EconomicsMoscowRussia

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