Towards semantically meaningful feature spaces for the characterization of video content
Efficient procedures for browsing, filtering, sorting or retrieving pictorial content require accurate content characterization. Of particular interest are representations based on semantically meaningful feature spaces, capable of capturing properties such as violence, sex or profanity. In this work we report on a first step towards this goal, the design of a stochastic model for video editing which provides a transformation from the image space to a low-dimensional feature space where categorization by degree of action can be easily accomplished.