On-line Television Stream Classification by Genre
On-line Television Stream Classification by Genre is a scholarly work, published in 2018 in ''Baltic Journal of Modern Computing''. The main subjects of the publication include line, digital forensics, automatic summarization, audio signal processing, and computer science. 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.