Detecting Violent Scenes in Movies by Auditory and Visual Cues

To detect violence in movies, we present a three-stage method integrating visual and auditory cues. In our method, those shots with potential violent content are first identified according to universal film-making rules. A modified semi-supervised learning technique based on semi-supervised cross feature learning (SCFL) is exploited, since it is capable to combine different types of features and use unlabeled data to improve the classification performance. Then, typical violence-related audio effects are further detected for the candidate shots, and we manage to transform the confidences outputted by the classifiers of various audio events into a shot-based violence score. Finally, the first two-stage probabilistic outputs are integrated in a boosting way to generate the final inference. The experimental results on four typical action movies preliminarily show the effectiveness of our method.