A naive mid-level concept-based fusion approach to violence detection in Hollywood movies

  • Authors:

  • Bogdan Ionescu;Jan Schlüter;Ionut Mironica;Markus Schedl

  • Affiliations:

  • University Politehnica of Bucharest, Bucharest, Romania;Austrian Research Institute for Artificial Intelligence, Vienna, Austria;University Politehnica of Bucharest, Bucharest, Romania;Johannes Kepler Universität, Linz, Austria

  • Venue:
  • Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
  • Year:
  • 2013

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Abstract

In this paper we approach the issue of violence detection in typical Hollywood productions. Given the high variability in appearance of violent scenes in movies, training a classifier to predict violent frames directly from visual or/and auditory features seems rather difficult. Instead, we propose a different perspective that relies on fusing mid-level concept predictions that are inferred from low-level features. This is achieved by employing a bank of multi-layer perceptron classifiers featuring a dropout training scheme. Experimental validation conducted in the context of the Violent Scenes Detection task of the MediaEval 2012 Multimedia Benchmark Evaluation show the potential of this approach that ranked first among 34 other submissions in terms of precision and F1-score.