Video object summarization in the MPEG-4 compressed domain
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Summarization of video content is necessary in order to reduce the large amount of data involved in video retrieval. In a frame-based digital video retrieval framework, this is achieved by representing the content of a video sequence using key frames. Similarly, in an object-based framework, such as the one suggested by the MPEG-4 standard, video object planes can be used for summarization of video object content. We propose a method for key video object plane selection using the shape information in the MPEG-4 compressed domain. Two popular shape distance measures, the Hamming and Hausdorff distance measures, are employed to measure the similarities between the approximated shapes of the video objects. The corresponding algorithms, with different implementation complexity and computation tradeoffs, select key video object planes that represent efficiently the salient content of the video objects.
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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 | 0.000 | 0.000 |
| 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