TubeFiler - an Automatic Web Video Categorizer

Damian Borth, Jörn Hees, Markus Koch, Adrian Ulges, Christian Schulze, Thomas Breuel, Roberto Paredes
In: ACM (ed.) ACM Mutimedia, Beijing, China, ACM, 10/2009

Abstract:

While current web video platforms offer only limited support for a taxonomy-based browsing, hierarchies are powerful tools for organizing content in other application areas. To overcome this limitation, we present a framework called TubeFiler. Its two key features are an automatic multi-modal categorization of videos into a genre hierarchy, and a support of additional fine-grained hierarchy levels based on unsupervised learning. We present experimental results on real-world YouTube clips with a 2-level 46-category genre hierarchy, indicating that - though the problem is clearly challenging - good category suggestions can be achieved. For example, if TubeFiler suggests 5 categories, it hits the right one (or its supercategory) in 91.8% of cases.

Files:

  http://madm.dfki.de/demo/tubefiler/
  google_challenge-tubefiler.pdf

BibTex:

@inproceedings{ BORT2009,
	Title = {TubeFiler - an Automatic Web Video Categorizer},
	Author = {Damian Borth and Jörn Hees and Markus Koch and Adrian Ulges and Christian Schulze and Thomas Breuel and Roberto Paredes},
	Editor = {ACM},
	BookTitle = {ACM Mutimedia},
	Month = {10},
	Year = {2009},
	Publisher = {ACM}
}

     
Last modified:: 30.08.2016