Name
Abstract | ||
|---|---|---|
The Informedia team participated in the tasks of Rushes summarization, high-level feature extraction and event detection in surveillance video. For the rushes summarization, our basic idea was to use subsampled video at the appropriate rate, showing almost the whole video faster, and then modify the result to remove garbage frames. Sinply subsampling the frames proved to be the best method for summarizing BBC rushes video, with other improvements not improving the basic inclusion rate, nor appreciably affecting the other subjective metrics. For the high-level feature detection, we trained exclusively on TRECVID’05 data and trying to assess and predict the reliability of the detectors. The voting scheme for combining multiple classifiers performed best, marginally better than trying to predict the best classifier based on a robustness calculation from within dataset cross-domain performance. For event detection, we found that the overall approach was effective at characterizing a presegmented event in the training data, but lack of event segmentation (information about the duration of an event and the existence of a known event resulted in a dramatically lower score in the official evaluation. |
Year | Venue | Field |
|---|---|---|
2008 | TRECVID | Training set,Automatic summarization,Garbage,Voting,Pattern recognition,Computer science,Segmentation,Robustness (computer science),Feature extraction,Artificial intelligence,Classifier (linguistics),Machine learning |
DocType | Citations | PageRank |
Conference | 3 | 0.41 |
References | Authors | |
11 | 10 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alexander G. Hauptmann | 1 | 7472 | 558.23 |
| Robert V. Baron | 2 | 605 | 221.60 |
| Ming-yu Chen | 3 | 902 | 79.29 |
| Michael G. Christel | 4 | 1170 | 157.47 |
| Wei-hao Lin | 5 | 679 | 41.81 |
| Xinghua Sun | 6 | 4 | 3.15 |
| Víctor Valdés | 7 | 64 | 8.30 |
| Jun Yang | 8 | 2762 | 241.66 |
| Lily B. Mummert | 9 | 307 | 63.38 |
| Steven W. Schlosser | 10 | 299 | 23.66 |