A shot classification method of selecting effective key-frames for video browsing
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
The indexable web is more than 11.5 billion pages
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
Practical elimination of near-duplicates from web video search
Proceedings of the 15th international conference on Multimedia
Automatic video tagging using content redundancy
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Learning to rank videos personally using multiple clues
Proceedings of the ACM International Conference on Image and Video Retrieval
Real-time near-duplicate elimination for web video search with content and context
IEEE Transactions on Multimedia - Special issue on integration of context and content
Content redundancy in YouTube and its application to video tagging
ACM Transactions on Information Systems (TOIS)
Correlation-based retrieval for heavily changed near-duplicate videos
ACM Transactions on Information Systems (TOIS)
Near-duplicate video retrieval: Current research and future trends
ACM Computing Surveys (CSUR)
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The explosive growth of information technology and digital content industry stimulates various video applications over the Internet. Since it is quite easy to copy, reformat, modify and republish video files on the websites, similarity/duplicate detection and measurement is essential to identify the excessive content duplication, so as to facilitate effective video search and intelligence propriety protection as well. In this paper, we propose a novel signature-based approach for duplicate video comparison. The so-called video histogram scheme counts the numbers of video’s frames that are closest to a set of representative seed vectors chosen from the feature space of the training data set in advance. Then all the numbers are normalized to generate the signature of the video for further comparison. As our signature is a compact fixed-size vector with low dimension for each video, it requires less storage and computation cost than previous methods. The experiments show that our approach is both efficient and effective for web video duplicate detection.