Evaluating detection of near duplicate video segments
Proceedings of the ACM International Conference on Image and Video Retrieval
The IMMED project: wearable video monitoring of people with age dementia
Proceedings of the international conference on Multimedia
Sequence kernels for clustering and visualizing near duplicate video segments
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
| Hi-index | 0.00 |
In this paper, we propose a novel method inspired by the bio-informatics domain to parse a rushes video into scenes and takes. The Smith-Waterman algorithm provides an efficient way to compare sequences by comparing segments of all possible lengths and optimizing the similarity measure. We propose to adapt this method in order to detect repetitive sequences in rushes video. Based on the alignments found, we can parse the video into scenes and takes. By comparing takes together, we can select the most complete take in each scene. This method is evaluated on several rushes videos from the TRECVID BBC Rushes Summarization campaign.