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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%.