Internet Multimedia Search and Mining Authors: Xian-Sheng Hua, Marcel Worring, and Tat-Seng Chua
In this chapter, we address the visual learning of automatic concept detectors from web video as available from services like YouTube. While allowing a much more efficient, flexible, and scalable concept learning compared to expert labels, web-based detectors perform poorly when applied to different domains (such as specific TV channels). We address this domain change problem using a novel approach, which – after an initial training on web content – performs a highly efficient online adaptation on the target domain.
In quantitative experiments on data from YouTube and from the TRECVID campaign, we first validate that domain change appears to be the key problem for web-based concept learning, with much more significant impact than other phenomena like label noise. Second, the proposed adaptation is shown to improve the accuracy of web-based detectors significantly, even over SVMs trained on the target
domain. Finally, we extend our approach with active learning such that adaptation can be interleaved with manual annotation for an efficient exploration of novel domains.
The authors cite authority for the proposition that by 2013 that 91% of all Internet traffic will be digital video.
Perhaps, perhaps not, but in any event, “concept detection” is an important aid to topic map authors working with digital video.
- Later research on “concept detection” in digital video? (annotated bibliography)
- Use in library contexts? (3-5 pages, citations)
- How would you design human augmentation of automated detection? (project)