On the detection and recognition of television commercials
ICMCS '97 Proceedings of the 1997 International Conference on Multimedia Computing and Systems
Real time repeated video sequence identification
Computer Vision and Image Understanding
Fast and robust short video clip search using an index structure
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
Detecting image near-duplicate by stochastic attributed relational graph matching with learning
Proceedings of the 12th annual ACM international conference on Multimedia
A repeated video clip identification system
Proceedings of the 13th annual ACM international conference on Multimedia
A probabilistic template-based approach to discovering repetitive patterns in broadcast videos
Proceedings of the 13th annual ACM international conference on Multimedia
A quick search method for audio and video signals based on histogram pruning
IEEE Transactions on Multimedia
ARGOS: automatically extracting repeating objects from multimedia streams
IEEE Transactions on Multimedia
Mining TV broadcasts for recurring video sequences
Proceedings of the ACM International Conference on Image and Video Retrieval
A fast video copy detection approach by dynamic programming
PCM'10 Proceedings of the 11th Pacific Rim conference on Advances in multimedia information processing: Part I
TV program segmentation using multi-modal information fusion
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Efficient mining of repetitions in large-scale TV streams with product quantization hashing
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
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Automatically discovering repetitive clips from large video database is a challenging problem due to the enormous computational cost involved in exploring the huge solution space. Without any a priori knowledge of the contents, lengths and total number of the repetitive clips, we need to discover all of them in the video database. To address the large computational cost, we propose a novel method which translates repetitive clip mining to the continuous path finding problem in a matching trellis, where sequence matching can be accelerated by taking advantage of the temporal redundancies in the videos. By applying the locality sensitive hashing (LSH) for efficient similarity query and the proposed continuous path finding algorithm, our method is of only quadratic complexity of the database size. Experiments conducted on a 10.5-hour TRECVID news dataset have shown the effectiveness, which can discover repetitive clips of various lengths and contents in only 25 minutes, with features extracted off-line.