Finding Meaning on YouTube: Tag Recommendation and Category Discovery
Venue
Computer Vision and Pattern Recognition, IEEE (2010)
Publication Year
2010
Authors
George Toderici, Hrishikesh Aradhye, Marius Pasca, Luciano Sbaiz, Jay Yagnik
BibTeX
Abstract
We present a system that automatically recommends tags for YouTube videos solely
based on their audiovisual content. We also propose a novel framework for
unsupervised discovery of video categories that exploits knowledge mined from the
World-Wide Web text documents/searches. First, video content to tag association is
learned by training classifiers that map audiovisual content-based features from
millions of videos on YouTube.com to existing uploader-supplied tags for these
videos. When a new video is uploaded, the labels provided by these classifiers are
used to automatically suggest tags deemed relevant to the video. Our system has
learned a vocabulary of over 20,000 tags. Secondly, we mined large volumes of Web
pages and search queries to discover a set of possible text entity categories and a
set of associated is-A relationships that map individual text entities to
categories. Finally, we apply these is-A relationships mined from web text on the
tags learned from audiovisual content of videos to automatically synthesize a
reliable set of categories most relevant to videos -- along with a mechanism to
predict these categories for new uploads. We then present rigorous rating studies
that establish that: (a) the average relevance of tags automatically recommended by
our system matches the average relevance of the uploader-supplied tags at the same
or better coverage and (b) the average precision@K of video categories discovered
by our system is 70% with K=5.
