Paper Info
Title
Dynamic vocabularies for web-based concept detection by trend discovery
Abstract
We present a novel approach towards automatic vocabulary selection for video concept detection. Our key idea is to expand concept vocabularies with trending topics that we mine automatically on other media like Wikipedia or Twitter. We evaluate several strategies for extending concept detection to auto-detect these topics in new videos, either by linking them to a static concept vocabulary, by a visual learning of trends on the fly, or by an expansion of the vocabulary. Our study on 6,800 YouTube clips and the top 23 target trends (covering a timespan of 6 months) demonstrates that a direct visual classification of trends (by a "live" learning on trend videos) outperforms an inference from static vocabularies. However, further improvements can be achieved by a combination of both approaches.
Year
DOI
Venue
2012
10.1145/2393347.2396361
ACM Multimedia 2001
Keywords
Field
DocType
direct visual classification,youtube clip,automatic vocabulary selection,key idea,visual learning,video concept detection,static concept vocabulary,concept detection,trend discovery,dynamic vocabulary,concept vocabulary,static vocabulary,web-based concept detection,social media
World Wide Web,Social media,Information retrieval,Computer science,Inference,On the fly,Visual learning,Web application,Multimedia,Vocabulary,CLIPS
Conference
Citations 
PageRank 
References 
4
0.50
12
Authors
3
Name
Order
Citations
PageRank
Damian Borth176449.45
Adrian Ulges232826.61
Thomas M. Breuel32362219.10