Video keyframe production by efficient clustering of compressed chromaticity signatures (poster session)
Bibliographic record
Abstract
We develop a new low-dimensional video frame feature that is more insensitive to lighting change, motivated by color constancy work in physics-based vision, and apply the feature to keyframe production using hierarchical clustering. The new feature has the further advantage of more expressively capturing image information and as a result produces a very succinct set of keyframes for any video. Because we effectively reduce any video to the same lighting conditions, we can produce a universal basis on which to project video frame features. We carry out clustering efficiently by adapting a hierarchical clustering data structure to temporally-ordered clusters. Using a new multi-stage hierarchical clustering method, we merge clusters based on the ratio of cluster variance to variance of the parent node, merging only adjacent clusters, and then follow with a second round of clustering. The second stage merges clusters incorrectly split in the first round by the greedy hierarchical algorithm, and as well merges non-adjacent clusters to fuse near-repeat shots. The new summarization method produces a very succinct set of keyframes for videos, and results are excellent.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".