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Detecting violent content in Hollywood movies by mid-level audio representations

Movie violent content detection e.g., for providing automated youth protection services is a valuable video content analysis functionality. Choosing discriminative features for the representation of video segments is a key issue in designing violence detection algorithms. In this paper, we employ mid-level audio features which are based on a Bag-of-Audio Words (BoAW) method using Mel-Frequency Cepstral Coefficients (MFCCs). BoAW representations are constructed with two different methods, namely the vector quantization-based (VQ-based) method and the sparse coding-based (SC-based) method. We choose two-class support vector machines (SVMs) for classifying video shots as (non-)violent. Our experiments on detecting violent video shots in Hollywood movies show that the mid-level audio features provide promising results. Additionally, we establish that the SC-based method outperforms the VQ-based one. More importantly, the SC-based method outperforms the unimodal submissions in the MediaEval Violent Scenes Detection (VSD) task, except one vision-based method in terms of average precision.