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NII, Japan at MediaEval 2012 Violent Scenes Detection Affect Task

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NII, Japan at MediaEval 2012 Violent Scenes Detection Affect Task

  1. 1. NII, Japan at MediaEval 2012Violent Scenes Detection TaskVu Lam(1), Duy-Dinh Le(2), Sang-Phan Le(2)Shin’ichi Satoh(2), Duc Anh Duong(3)(1) University of Science lqvu@fit.hcmus.edu.vn(2) National Institute of Informatics {ledduy,plsang,satoh}@nii.ac.jp(3) University of Information Technology ducda@uit.edu.vn
  2. 2. Our approaches Using NII-KAORI-SECODE, a general frame-work for semantic concept detection. Using 5 keyframes per shot. Try to apply shot-based features using the global features (color moments, color histogram, edge orientation histogram, and local binary patterns) for violent scenes detection. Evaluate the performance of late fusion with visual attributes (blood, fights, gore, car chase, gore, cold arms, firearm).10/5/2012 NII, Japan at MediaEval 2012 2 Violent Scenes Detection Task
  3. 3. Shot-based features10/5/2012 NII, Japan at MediaEval 2012 3 Violent Scenes Detection Task
  4. 4. Classifier learning & Experiment Classifier learning: LibSVM10/5/2012 NII, Japan at MediaEval 2012 4 Violent Scenes Detection Task
  5. 5. NII Runs10/5/2012 NII, Japan at MediaEval 2012 5 Violent Scenes Detection Task
  6. 6. Discussion & Future work The definition of violence is so general The length of shots are very diverse, many shots are very short, and might be easily classified as non- violent shots based on the definition. Fusion of the violence detection results with other visual attributes results cannot improve the performance. Future work is to study how to use visual attributes to represent violent scenes10/5/2012 NII, Japan at MediaEval 2012 6 Violent Scenes Detection Task

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