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Journal of Computer and Systems Sciences International

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01-05-2020 | ARTIFICIAL INTELLIGENCE

Training a Classifier by Descriptors in the Space of the Radon Transform

Authors: N. A. Samsonov, A. N. Gneushev, I. A. Matveev

Published in: Journal of Computer and Systems Sciences International | Issue 3/2020

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Abstract

The problem of detecting objects in images is solved by training the classifier by the descriptors constructed based on the local Radon transform of the gradient field of the image. The space of the Radon transform is considered as the Hough space accumulator in which projections are constructed. The set of local projections forms a descriptor of the region related to the object that is a generalization of the known histogram of oriented gradients (HOG) descriptor. The issues of the effect of the approximation of the Radon transform contribution function, the form of local normalization, and the number of directions in the projection histograms on the results of detecting pedestrians are investigated. The results produced by the proposed descriptor are compared with the results obtained using the HOG descriptor and convolutional neural networks (CNN) based on the ResNext and MobileNet architectures on the INRIA and CityScapes databases.

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Title: Training a Classifier by Descriptors in the Space of the Radon Transform
Authors: N. A. Samsonov
A. N. Gneushev
I. A. Matveev
Publication date: 01-05-2020
Publisher: Pleiades Publishing
Published in: Journal of Computer and Systems Sciences International / Issue 3/2020
Print ISSN: 1064-2307
Electronic ISSN: 1555-6530
DOI: https://doi.org/10.1134/S1064230720030053

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