Efficient labelling algorithms for the maximum noncrossing matching problem
Discrete Applied Mathematics - Special issue on new frontiers in the theory and practice of combinatorial optimization: applications in manufacturing and VLSI design
The merge/purge problem for large databases
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Efficient clustering of high-dimensional data sets with application to reference matching
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Interactive deduplication using active learning
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Detecting image near-duplicate by stochastic attributed relational graph matching with learning
Proceedings of the 12th annual ACM international conference on Multimedia
Detection of video sequences using compact signatures
ACM Transactions on Information Systems (TOIS)
Statistical similarity search applied to content-based video copy detection
ICDEW '05 Proceedings of the 21st International Conference on Data Engineering Workshops
A fast shot matching strategy for detecting duplicate sequences in a television stream
Proceedings of the 2nd international workshop on Computer vision meets databases
An embedded watermark technique in video for copyright protection
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Video copy detection: a comparative study
Proceedings of the 6th ACM international conference on Image and video retrieval
Merging the results of approximate match operations
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
UQLIPS: a real-time near-duplicate video clip detection system
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Spatiotemporal sequence matching for efficient video copy detection
IEEE Transactions on Circuits and Systems for Video Technology
Video Fingerprinting by Using Boosted Features
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
Multimodal video copy detection applied to social media
WSM '09 Proceedings of the first SIGMM workshop on Social media
Scalable detection of partial near-duplicate videos by visual-temporal consistency
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Fast min-hashing indexing and robust spatio-temporal matching for detecting video copies
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Real-time keyframe extraction towards video content identification
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
International Journal of Multimedia Data Engineering & Management
Content-based copy detection through multimodal feature representation and temporal pyramid matching
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Hi-index | 0.00 |
Sites to share user-created video clips such as YouTube and Yahoo Video have become greatly popular in recent years. One of the challenges of such sites is, however, to prevent video clips that violate copyrights by illegally copying and editing scenes from other videos. Due to the sheer number of clips uploaded every day, automatic methods to detect (illegally) copied video clips in a large collection are desirable. Toward this problem, in this paper, we present a novel framework, termed as Video Linkage, that is based on the record linkage techniques. Our proposal is based on the observations that: (1) a video clip can be represented as a "group" of key frames, (2) two video clips are deemed to be similar if two groups of key frames are similar as a whole - i.e., the similarity of two video clips can be measured by means of graph-based similarity measures such as maximal cardinality bipartite matching, and (3) if a video clip va is copied to vb, then va and vb must be somehow similar, but not all similar video clips are illegally copied ones - i.e., similar videos can be used as a filter for fast detection of copied videos. The validity of our observations and Video Linkage technique is thoroughly evaluated using both real and synthetic data sets - i.e., on average, our proposals achieved 0.94 as precision and 0.93 as recall across 10 genres and 6 editing patterns.