Publication Type
Journal Article
Version
publishedVersion
Publication Date
10-2010
Abstract
With the prosperity of video-sharing websites such as YouTube, the amount of community-contributed video has increased dramatically. Reportedly more than 65,000 new videos were uploaded to YouTube every day in July 2006 and it's estimated that 20 hours of new videos were uploaded to the site every minute in May 2009. In addition to the huge volume of video data, the social Web provides rich contextual and social resources associated with videos. These resources include title, tags, thumbnails, related videos, and user and community information, as illustrated in Figure 1. While billions of user-generated videos accompanied with rich-media information have enriched the Web-browsing experience, this scenario brings new opportunities and challenges for effective and efficient searching, mining, and organizing of large-scale Web videos.
Keywords
Social Web, Data Driven, Web Video, Near Duplicate Detection, Annotation, Categorization
Discipline
Data Storage Systems
Research Areas
Intelligent Systems and Optimization
Publication
IEEE MultiMedia
Volume
17
Issue
4
First Page
58
Last Page
68
ISSN
1070-986X
Identifier
10.1109/MMUL.2010.46
Publisher
Institute of Electrical and Electronics Engineers
Citation
1
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.