TubeTagger -­ YouTube-based Concept Detection

Adrian Ulges; Markus Koch; Damian Borth; Thomas Breuel

In: Proceddings of the International Workshop on Internet Multimedia Mining. International Workshop on Internet Multimedia Mining (IMM-09), located at IEEE ICDM 2009, December 6, Miami, FL, USA, IEEE Computer Society, 12/2009.


We present TubeTagger, a concept-based video retrieval system that exploits web video as an information source. The system performs a visual learning on YouTube clips (i.e., it trains detectors for semantic concepts like "soccer" or "windmill"), and a semantic learning on the associated tags (i.e., relations between concepts like "swimming" and "water" are discovered). This way, a text-based video search free of manual indexing is realized. We present a quantitative study on web-based concept detection comparing several features and statistical models on a large-scale dataset of YouTube content. Beyond this, we report several key findings related to concept learning from YouTube and its generalization to different domains, and illustrate certain characteristics of YouTube-learned concepts, like focus of interest and redundancy. To get a hands-on impression of web-based concept detection, we invite researchers and practitioners to test our web demo.


Weitere Links

tubetagger.pdf (pdf, 4 MB )

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence