TubeTagger - YouTube-based Concept Detection

Abstract

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.