On-line Television Stream Classification by Genre

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.