Linking visual concept detection with viewer demographics
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Tag suggestion on youtube by personalizing content-based auto-annotation
Proceedings of the ACM multimedia 2012 workshop on Crowdsourcing for multimedia
Dynamic vocabularies for web-based concept detection by trend discovery
Proceedings of the 20th ACM international conference on Multimedia
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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.