Learning representations for affective video understanding
Proceedings of the 21st ACM international conference on Multimedia
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We present results from a large study with 200 participants who watched short excerpts from TV programmes and assigned mood labels. The agreement between labellers was evaluated, showing that an overall consensus exists. Multiple mood terms could be reduced to two principal dimensions, the first relating to the seriousness or light-heartedness of programmes, the second describing the perceived pace. Automatic classification of both mood dimensions was possible to a high degree of accuracy, reaching more than 95% for programmes with very clear moods. The influence of existing human generated genre labels was evaluated, showing that they were closely related to the first mood dimension and helped to distinguish serious form humorous programmes. The pace of programmes however could be more accurately classified when features based on audio and video signal processing were used.