Bibliographic record
Abstract
Efficient browsing of long video sequences is a key tool in visual surveillance, e.g., for postevent video forensics, but can also be used for fast review of motion pictures and home videos. While frame skipping (fixed or adaptive) is straightforward to implement, its performance is quite limited. Although more efficient techniques have been developed, such as video summarization and video montage, they lose either the temporal or semantic context of events. A recently proposed method called video synopsis deals with some of these issues but involves multiple processing stages and is fairly complex. Video condensation, that we propose here, is novel in the way information is removed from the space-time video volume, is conceptually simple and relatively easy to implement. We introduce the concept of a video ribbon inspired by that of a seam recently proposed for image resizing. We recursively carve ribbons out by minimizing an activity-aware cost function using dynamic programming. The ribbon model we develop is flexible and permits an easy adjustment of the compromise between temporal condensation ratio and anachronism of events. We also propose sliding-window ribbon carving to handle streaming video and demonstrate the method's efficiency on motor and pedestrian traffic data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".