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This paper describes the team MTM participation in Violent Scenes Detection (VSD) task of the MediaEval 2014 campaign. We propose an approach to the problem of detecting violence, which is based on probabilistic graphical models using Mel-frequency cepstral coefficients (MFCCs) as audio feature. In our approach, we employ Dynamic Bayesian Networks (DBNs) to(More)
This paper describes the team MTM participation in the MediaEval 2013 campaign. We submitted one run at shot level that explores spatial correlation between acoustic-visual features. The motion features are computed to represent the video.The Mel Frequency Cepstral Coefficients (MFCC) of the acoustic signal, and their first and second order derivatives are(More)
This paper reports a system developed for video browsing based on multimodal analysis. Our multimodal approach performs audio transcription for shot categorization (sports, weather, politics and economy) combining audio and visual information for theme categorization. Its main features include static and dynamic summaries, segmentation using face detection,(More)
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