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

Ch. 2. P. 14–23.

Савченко А. В., Белова Н. С., Milov V.

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.

Ключевые слова: распознавание изображений, Image recognition, probabilistic neural network, вероятностная нейронная сеть, sequential analysis, статистический последовательный анализ, иерархическая классификация, hierarchical recognition

В книге

Analysis of Images, Social Networks and Texts. 4th International Conference, AIST 2015, Yekaterinburg, Russia, April 9–11, 2015, Revised Selected Papers

Vol. 542: Series: Communications in Computer and Information Science. , Switzerland: Springer, 2015.