The ICL-TUM-PASSAU approach for the MediaEval 2015 "affective impact of movies" task

In this paper we describe the Imperial College London, Technische Universitat München and University of Passau (ICL+TUM+PASSAU) team approach to the MediaEval's "Affective Impact of Movies" challenge, which consists in the automatic detection of affective (arousal and valence) and violent content in movie excerpts. In addition to the baseline features, we computed spectral and energy related acoustic features, and the probability of various objects being present in the video. Random Forests, AdaBoost and Support Vector Machines were used as classification methods. Best results show that the dataset is highly challenging for both affect and violence detection tasks, mainly because of issues in inter-rater agreement and data scarcity