Automatic partitioning of full-motion video
Multimedia Systems
An Affine Invariant Interest Point Detector
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Fast and robust short video clip search using an index structure
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
Practical elimination of near-duplicates from web video search
Proceedings of the 15th international conference on Multimedia
Internet image archaeology: automatically tracing the manipulation history of photographs on the web
MM '08 Proceedings of the 16th ACM international conference on Multimedia
An efficient near-duplicate video shot detection method using shot-based interest points
IEEE Transactions on Multimedia
Scalable clip-based near-duplicate video detection with ordinal measure
Proceedings of the ACM International Conference on Image and Video Retrieval
Automatic video archaeology: tracing your online videos
Proceedings of second ACM SIGMM workshop on Social media
A quick search method for audio and video signals based on histogram pruning
IEEE Transactions on Multimedia
Fast similarity search and clustering of video sequences on the world-wide-web
IEEE Transactions on Multimedia
Near-Duplicate Keyframe Identification With Interest Point Matching and Pattern Learning
IEEE Transactions on Multimedia
Efficient video similarity measurement with video signature
IEEE Transactions on Circuits and Systems for Video Technology
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Facing the explosive growth of near-duplicate videos, video archaeology is quite desired to investigate the history of the manipulations on these videos. With the determination of derived videos according to the manipulations, a video migration map can be constructed with the pair-wise relationships in a set of near-duplicate videos. In this paper, we propose an improved video archaeology (I-VA) system by extending our previous work (Shen et al. 2010). The extensions include more comprehensive video manipulation detectors and improved techniques for these detectors. Specially, the detectors are used for two categories of manipulations, i.e., semantic-based manipulations and non-semantic-based manipulations. Moreover, the improved detecting algorithms are more stable. The key of I-VA is the construction of a video migration map, which represents the history of how near-duplicate videos have been manipulated. There are various applications based on the proposed I-VA system, such as better understanding of the meaning and context conveyed by the manipulated videos, improving current video search engines by better presentation based on the migration map, and better indexing scheme based on the annotation propagation. The system is tested on a collection of 12,790 videos and 3,481 duplicates. The experimental results show that I-VA can discover the manipulation relation among the near-duplicate videos effectively.