A System that Learns to Tag Videos by Watching Youtube

Adrian Ulges, Christian Schulze, Daniel Keysers, Thomas Breuel

In: Int. Conf. on Vision Systems (ICVS). International Conference on Computer Vision Systems (ICVS-2008) 6th May 12-15 Santorini Greece Seiten 415-424 Springer 5/2008.


We present a system that automatically tags videos, i.e. de- tects high-level semantic concepts like objects or actions in them. To do so, our system does not rely on datasets manually annotated for re- search purposes. Instead, we propose to use videos from online portals like as a novel source of training data, whereas tags pro- vided 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 quantita- tive 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.


2008-IUPR-31Jan_1134.pdf (pdf, 7 MB )

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence