Bibliographic record
Abstract
Video information, image processing and computer vision techniques are developing rapidly nowadays because of the availability of acquisition, processing and editing tools, which use current hardware and software systems. However, problems still remain in conveying this video data from enterprise video databases or emails to their end users. Limiting factors are the resource capabilities in distributed architectures, enterprise policies and the features of the users' terminals. The efficient use of image processing, video indexing and analysis techniques can provide users with solutions or alternatives. The paper presents a new algorithm for achieving video segmentation, indexing and key framing tasks. The algorithm is based on color histograms and uses a binary penetration technique. Although a lot has been done in this area, most work does not adequately consider the optimization of timing performance and processing storage. This is especially the case if the techniques are designed for use in run-time distributed environments. The main contribution is to blend high performance and storage criteria with the need to achieve effective results. Another issue is the heterogeneous run-time conditions. Thus, we designed a platform-independent XML schema of our video service. We will present our implemented prototype to realize a video Web service over the World Wide Web.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".