A System That Learns to Tag Videos by Watching Youtube

We present a system that automatically tags videos, i.e. detects high-level semantic concepts like objects or actions in them. To do so, our system does not rely on datasets manually annotated for research purposes. Instead, we propose to use videos from online portals like youtube.com as a novel source of training data, whereas tags provided by users during upload serve as ground truth annotations. This allows our system to learn autonomously by automatically downloading its training set. The key contribution of this work is a number of large-scale quantitative experiments on real-world online videos, in which we investigate the influence of the individual system components, and how well our tagger generalizes to novel content. Our key results are: (1) Fair tagging results can be obtained by a late fusion of several kinds of visual features. (2) Using more than one keyframe per shot is helpful. (3) To generalize to different video content (e.g., another video portal), the system can be adapted by expanding its training set. keywords: content-based video retrieval, automatic video annotation, online videos