Human annotations (titles and tags) of web videos facilitate most web video applications. However, the raw tags are noisy, sparse and structureless, which limit the effectiveness of tags. In this paper, we propose a tag transformer schema to solve these problems. We first eliminate those imprecise and meaningless tags with Wikipedia, and then transform the remaining tags to the Wikipedia category set to gather a precise, complete and structural description of the tags. Our experimental results on web video categorization demonstrate the superiority of the transformed space. We also apply tag transformer into the first study of using Wikipedia category system to structurally recommend the related videos. The online user study of the demo system suggests that our method could bring fantastic experience to the web users.
[1]
Yongdong Zhang,et al.
Google challenge: incremental-learning for web video categorization on robust semantic feature space
,
2009,
ACM Multimedia.
[2]
Roelof van Zwol,et al.
Classifying tags using open content resources
,
2009,
WSDM '09.
[3]
Tao Mei,et al.
VideoReach: an online video recommendation system
,
2007,
SIGIR.
[4]
Rada Mihalcea,et al.
Using Wikipedia for Automatic Word Sense Disambiguation
,
2007,
NAACL.
[5]
Dong Liu,et al.
Tag ranking
,
2009,
WWW '09.
[6]
J. Giles.
Internet encyclopaedias go head to head
,
2005,
Nature.
[7]
Mor Naaman,et al.
Why we tag: motivations for annotation in mobile and online media
,
2007,
CHI.
[8]
Kilian Q. Weinberger,et al.
Resolving tag ambiguity
,
2008,
ACM Multimedia.