Determination of emotional content of video clips by low-level audiovisual features

Affective analysis of video content has greatly increased the possibilities of the way we perceive and deal with media. Different kinds of strategies have been tried, but results are still opened to improvements. Most of the problems come from the lack of standardized test set and real affective models. In order to cope with these issues, in this paper we describe the results of our work on the determination of affective models for evaluation of video clips using audiovisual low-level features. The affective models were developed following two classes of psychological theories of affect: categorial and dimensional. The affective models were created from real data, acquired through a series of user experiments. They reflect the affective state of a viewer after watching a certain scene from a movie. We evaluate the detection of Pleasure, Arousal and Dominance coefficients as well as the detection rate of six affective categories. For this end, two Bayesian network topologies are used, a Hidden Markov Model and an Autoregressive Hidden Markov Model. The measurements were done using audio-only models, video-only models and fused models. Fusion is done using two different methods, a Decision Level Fusion and Feature Level Fusion. All tests were conducted using localized affective models, both categorial and dimensional. Results are presented in terms of detection rate and accuracy for affective families, affective dimensions and probabilistic networks. Arousal was the best detected dimension, followed by dominance and pleasure.