Adult Video Content Detection Using Machine Learning Techniques

Automatic adult video detection is a problem of interest to many organizations around the world. The aim is to restrict the easy access of underage youngsters to such potentially harmful material. Most of the existing techniques are mere extensions of image categorization approaches. In this paper we propose a video genre classification technique tuned specifically for adult content detection by considering cinematographic principles. Spatial and temporal simple features are used with machine learning algorithms to perform the classification into two classes: adult and non-offensive video material. Shot duration and camera motion, are the temporal domain features, and skin detection and color histogram are the spatial domain ones. Using two data sets of 7 and 15 hours of video material, our experiments comparing two different SVM classifiers achieved an accuracy of 94.44%.

• Publication year

  2012

• Venue

  2012 Eighth International Conference on Signal Image Technology and Internet Based Systems

• Publication date

  2012-11-01

• Fields of study

  Computer Science

• Identifiers
  DOI 10.1109/SITIS.2012.143
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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