Affiliation:
1. Telefonica Research, and Penn State University
2. L3S Research Center
3. RMIT University
Abstract
The emergence of large-scale social Web communities has enabled users to share online vast amounts of multimedia content. An analysis of YouTube reveals a high amount of redundancy, in the form of videos with overlapping or duplicated content. We use robust content-based video analysis techniques to detect overlapping sequences between videos. Based on the output of these techniques, we present an in-depth study of duplication and content overlap in YouTube, and analyze various dependencies between content overlap and meta data such as video titles, views, video ratings, and tags. As an application, we show that content-based links provide useful information for generating new tag assignments. We propose different tag propagation methods for automatically obtaining richer video annotations. Experiments on video clustering and classification as well as a user evaluation demonstrate the viability of our approach.
Funder
Seventh Framework Programme
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Science Applications,General Business, Management and Accounting,Information Systems
Cited by
21 articles.
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