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Deep learning application for box-office evaluation of images

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dc.contributor.authorEfremtsev, V. G.
dc.contributor.authorEfremtsev, N. G.
dc.contributor.authorTeterin, E. P.
dc.contributor.authorGantsovsky, V. V.
dc.contributor.authorTeterin, P. E.
dc.contributor.authorТетерин, Пётр Евгеньевич
dc.date.accessioned2024-11-25T17:16:25Z
dc.date.available2024-11-25T17:16:25Z
dc.date.issued2020
dc.description.abstract© 2020, Institution of Russian Academy of Sciences. All rights reserved.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 hy-perparameters of the neural network we achieved a classification accuracy of at least 98%.
dc.format.extentС. 127-132
dc.identifier.citationDeep learning application for box-office evaluation of images / Efremtsev, V.G. [et al.] // Computer Optics. - 2020. - 44. - № 1. - P. 127-132. - 10.18287/2412-6179-CO-515
dc.identifier.doi10.18287/2412-6179-CO-515
dc.identifier.urihttps://www.doi.org/10.18287/2412-6179-CO-515
dc.identifier.urihttps://www.scopus.com/record/display.uri?eid=2-s2.0-85082048185&origin=resultslist
dc.identifier.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS_CPL&DestLinkType=FullRecord&UT=WOS:000520038900016
dc.identifier.urihttps://openrepository.mephi.ru/handle/123456789/20444
dc.relation.ispartofComputer Optics
dc.titleDeep learning application for box-office evaluation of images
dc.typeArticle
dspace.entity.typePublication
oaire.citation.issue1
oaire.citation.volume44
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