Abstract
In this paper we present our research towards the detection of violent scenes in movies, employing fusion methodologies, based on learning. Towards this goal, a multi-step approach is followed: initially, automated auditory and visual processing and analysis is performed in order to estimate probabilistic measures regarding particular audio and visual related classes. At a second stage, a meta-classification architecture is adopted, which combines the audio and visual information, in order to classify mid-term video segments as “violent” or “non-violent”. The proposed scheme has been evaluated on a real dataset from 10 films.
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Giannakopoulos, T., Makris, A., Kosmopoulos, D., Perantonis, S., Theodoridis, S. (2010). Audio-Visual Fusion for Detecting Violent Scenes in Videos. In: Konstantopoulos, S., Perantonis, S., Karkaletsis, V., Spyropoulos, C.D., Vouros, G. (eds) Artificial Intelligence: Theories, Models and Applications. SETN 2010. Lecture Notes in Computer Science(), vol 6040. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12842-4_13
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DOI: https://doi.org/10.1007/978-3-642-12842-4_13
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