文章基本信息

标题：On-line Television Stream Classification by Genre
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作者：Karlis Martins Briedis ; Karlis Freivalds
期刊名称：Baltic Journal of Modern Computing
印刷版ISSN：2255-8942
电子版ISSN：2255-8950
出版年度：2018
卷号：6
期号：3
页码：1-12
DOI：10.22364/bjmc.2018.6.3.02
出版社：Vilnius University, University of Latvia, Latvia University of Agriculture, Institute of Mathematics and Informatics of University of Latvia
摘要：Convolutional neural networks (CNNs) have become the state-of-the-art solution for image classification and other related problems. This paper investigates the use of CNNs’ features for on-line television stream classification by genre of the programme. As most existing offline classification solutions propose the use of low level audio-visual video descriptors, this paper compares the precision achieved by simple structure multi-layer perceptrons (MLP) and long short-term memory (LSTM) recurrent neural networks (RNNs) using either low level visual and audial descriptors or activations of InceptionV3 CNN’s global pooling layer as features. The best real-time classification accuracy on evaluation data set of 71,6% was achieved by an LSTM RNN of CNN features, supporting the use of CNNs for television genre classification.
关键词：television genre classification; television stream classification; video classification; neural networks; InceptionV3