Abstract
Tags associated with web videos play a crucial role in organizing and accessing large-scale video collections. However, the raw tag list (RawL) is usually incomplete, imprecise and unranked, which reduces the usability of tags. Meanwhile, compared with studies on improving the quality of web image tags, tags associated with web videos are not studied to the same extent. In this paper, we propose a novel web video tag enhancement approach called video retagging, which aims at producing the more complete, precise, and ranked retagged tag list (RetL) for web videos. Given a web video, video retagging first collect its textually and visually related neighbor videos. All tags attached to the neighbors are treated as possible relevant ones and then RetL is generated by inferring the degree of relevance of the tags from both global and video-specific perspectives, using two different graph based models. Two kinds of experiments, i.e., application-oriented video search and categorization and user-based subjective studies are carried out on a large-scale web video dataset, which demonstrate that in most cases, RetL is better than RawL in terms of completeness, precision and ranking.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 53-82 |
| Number of pages | 30 |
| Journal | Multimedia Tools and Applications |
| Volume | 55 |
| Issue number | 1 |
| DOIs | |
| State | Published - Oct 2011 |
Bibliographical note
Funding Information:Acknowledgments This work was supported by the National Basic Research Program of China (973 Program, 2007CB311100), National Nature Science Foundation of China (60902090), Beijing New Star Project on Science & Technology (2007B071), Co-building Program of Beijing Municipal Education Commission.
Keywords
- Graph based model
- Tag recommendation
- Tag refinement
- Video retagging
- Web video categorization
- Web video search