Interactive user oriented visual attention based video summarization and exploration framework
Bibliographic record
Abstract
An interactive user oriented high definition visual attention based video summarization and exploration framework is proposed to extract feature frames from a video collection and allow users to interactively explore those feature frames. It is based on previous work [1] that applies high definition visual attention algorithm mapping and multivariate mutual information to select a feature frames to represent each shot, then uses a self-organizing map to remove the redundant frames. After the video summary process, the extracted feature frames are connected into a network structure. Each node contains the information of the feature frame and the relation to other nodes. The relation between nodes are defined by clustering algorithms (self-organizing map, k-means, support vector machine, etc), expert systems (look-up table, fuzzy logic statement, etc) or any algorithm that defines similarity (sift, surf, etc). When a user select one node, depending on the user setting, the related nodes will be displayed onto a 2D canvas. In this way user is be able to interactively to browse through the whole video collection.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".