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A survey of deep learning methods and software tools for image classification and object detection

  • Representation, Processing, Analysis and Understanding of Images
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Abstract

Deep learning methods for image classification and object detection are overviewed. In particular we consider such deep models as autoencoders, restricted Boltzmann machines and convolutional neural networks. Existing software packages for deep learning problems are compared.

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Authors and Affiliations

  1. Lobachevsky State University of Nizhni Novgorod, Institute of Information Technologies, Mathematics and Mechanics, Nizhni Novgorod, Russian Federation

    P. N. Druzhkov & V. D. Kustikova

Authors
  1. P. N. Druzhkov
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  2. V. D. Kustikova
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Correspondence to P. N. Druzhkov.

Additional information

This paper uses the materials of the report submitted at the 9th Open German-Russian Workshop on Pattern Recognition and Image Understanding, held in Koblenz, December 1–5, 2014 (OGRW-9-2014).

The article published in the original.

Pavel Nikolaevich Druzhkov Born 1989 Graduated Lobachevsky State University of Nizni Novgorod in 2012. He is a junior research of the Lobachevsky State University of Nizhni Novgorod.

Research interests: machine learning and data mining, computer vision.

Number of publications (monographs and articles): 6.

Valentina Dmitrievna Kustikova Born 1987. Graduated in 2010, Lobachevsky State University of Nizhni Novgorod. Year of dissertation completion (Candidate’s, Doctoral): 2015, Candidate of Engineering Sciences. Assistent at the Lobachevsky State University of Nizhni Novgorod.

Research interests: computer vision, machine learning, parallel computing.

Number of publications (monographs and articles): 8.

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Druzhkov, P.N., Kustikova, V.D. A survey of deep learning methods and software tools for image classification and object detection. Pattern Recognit. Image Anal. 26, 9–15 (2016). https://doi.org/10.1134/S1054661816010065

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