Approximate nearest neighbors: towards removing the curse of dimensionality
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Locality-sensitive hashing scheme based on p-stable distributions
SCG '04 Proceedings of the twentieth annual symposium on Computational geometry
Scalable near identical image and shot detection
Proceedings of the 6th ACM international conference on Image and video retrieval
Summarizing audiovisual contents of a video program
EURASIP Journal on Applied Signal Processing
Generating comprehensible summaries of rushes sequences based on robust feature matching
Proceedings of the international workshop on TRECVID video summarization
Two-stage hierarchical video summary extraction to match low-level user browsing preferences
IEEE Transactions on Multimedia
Automated high-level movie segmentation for advanced video-retrieval systems
IEEE Transactions on Circuits and Systems for Video Technology
The trecvid 2008 BBC rushes summarization evaluation
TVS '08 Proceedings of the 2nd ACM TRECVid Video Summarization Workshop
| Hi-index | 0.00 |
This paper presents our approach to select relevant sequences from raw videos in order to generate summaries to Trecvid 2008 BBC Rush Task. Our system is composed of two major steps: First, the system detects "semantic" shot boundaries and keeps only non-redundant shots; then, the system estimates average motion for each shot, as a criterion of amount of information, to better share out the duration of the summary between remaining shots. The first step is based on a fast near-duplicate retrieval using Locality Sensitive Hashing (LSH) which provides results in few seconds (if we do not take into account decoding and encoding processes). The evaluation of Trecvid shows very promising results, since we ranked 17th over 43 runs, regarding redundancy measure (RE), and 18th for object and event inclusion (IN). These balanced results (most of best teams for the first criterion are among the latest for the second one) show that our method offers a quite good trade-off between false negatives (IN) and false positives (RE).