A Context-aware Description for Content Filtering on Video Sharing Social Networks

In this work, we investigate how much content-based visual information analysis can aid in filtering spam videos on video sharing social networks. That is a very challenging task, not only because of the high-level semantic concepts involved, but also because the diverse nature of social net-works prevents the use of constrained a priori information. In addition, a spam video is, by nature, context-dependent. We propose a context-aware description, which improves detection considerably in comparison with the baseline bags-of-visual-words model, by allowing us to incorporate the context of the video into the representation. Our model is evaluated in two challenging video dataset, showing very encouraging results.