Saturday , 17 August 2019
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Convolutional Neural Networks, Big Data and Deep Learning in Automatic Image Analysis

Mihnea Horia VREJOIU
National Institute for Research and Development in Informatics – ICI Bucharest
8-10 Mareșal Alexandru Averescu Av., 011455, Bucharest, Romania
mihnea.vrejoiu@ici.ro

Abstract: In recent years, there is an increasing amount of talk about artificial intelligence. What actually stands behind artificial intelligence today can be briefly summarized by the syntagm „artificial neural networks“, to which the adjective „deep“ has recently been added. Applications based on these have come to equate and even surpass human performance in many areas. One of the first fields in which they have been developed and in which they have gained a wide spread is artificial vision, respectively image recognition / classification. Without claiming to completely cover the subject, in this paper we propose a review, trying to capture as much intuitively as possible some essential elements and milestones of the history and evolution of artificial neural networks, with the new perspective offered in the last period by the availability of massive data (Big Data) used in conjunction with them as a major complementary, synergistic and convergent factor along with the quality and performance of the deep learning algorithms involved. Also, we analyze the elements and mechanisms that define and compose the convolutional networks in general, their functioning and their specificity with application in artificial vision, as well as two of the first such reference architectures, AlexNet and VGGNet, with their peculiarities and techniques used in the training, validation and testing processes.

Keywords: (deep) neural network, convolutional (neural) network, deep learning, big data, artificial vision, image recognition / classification.

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CITE THIS PAPER AS:
Mihnea Horia VREJOIU, Convolutional Neural Networks, Big Data and Deep Learning in Automatic Image Analysis, Romanian Journal of Information Technology and Automatic Control, ISSN 1220-1758, vol. 29(1), pp. 91-114, 2019. https://doi.org/10.33436/v29i1y201909