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Deep learning application for box-office evaluation of images

V.G. Efremtsev 1, N.G. Efremtsev 1, E.P. Teterin 2, P.E. Teterin 3, V.V. Gantsovsky 1

Independent researcher,
Kovrov State Technological Academy named after V.A.Degtyarev, Kovrov, Vladimir region, Russia;
National Research Nuclear University "MEPhI", Moscow, Russia

 PDF, 749 kB

DOI: 10.18287/2412-6179-CO-515

Pages: 127-132.

Full text of article: Russian language.

The possibility of application a convolutional neural network to assess the box-office effect of digital images is reviewed. We studied various conditions for sample preparation, optimizer algorithms, the number of pixels in the samples, the size of the training sample, color schemes, compression quality, and other photometric parameters in view of effect on training the neural network. Due to the proposed preliminary data preparation, the optimum of the architecture and hyperparameters of the neural network we achieved a classification accuracy of at least 98%.

deep learning, neural networks, image analysis.

Efremtsev VG, Efremtsev NG, Teterin EP, Teterin PE, Gantsovsky VV. Deep learning application for box-office evaluation of images. Computer Optics 2020; 44(1): 127-132. DOI: 10.18287/2412-6179-CO-515.

Authors thank for the support from National Research Nuclear University MEPhI in the framework of the Russian Academic Excellence Project (contract No. 02.a03.21.0005, 27.08.2013).


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