A unified approach to content-based indexing and retrieval of digital videos from television archives
Why this work is in the frame
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Bibliographic record
Abstract
This work addresses the development of a unified approach to content-based indexing and retrieval of digital videos fromtelevision archives. The proposed approach has been designed to deal with arbitrary television genres, making it suitablefor various applications. To achieve this goal, the main steps of a content-based video retrieval system are addressed in thiswork, namely: video segmentation, key-frame extraction, content-based video indexing and the video retrieval operation itself.Video segmentation is addressed as a typical TV broadcast structuring problem, which consists in automatically determiningthe boundaries of each broadcasted program (like movies, news, among others) and inter-program (for instance, commercials).Specifically, to segment the videos, Electronic Program Guide (EPG) metadata is combined with the detection of two specialcues, namely, audio cuts (silence) and dark monochrome frames. On the other hand, a color histogram-based approach performskey-frame extraction. Video indexing and retrieval are accomplished by using hashing and k-d tree methods, while visualsignatures containing color, shape and texture information are estimated for the key-frames, by using image and frequencydomain techniques. Experimental results with the dataset of a multimedia information system especially developed for managingtelevision broadcast archives demonstrate that our approach works efficiently, retrieving videos in 0.16 seconds on average andachieving recall, precision and F1 measure values, as high as 0.76, 0.97 and 0.86 respectively.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it