Video thumbnail extraction using video time density function and independent component analysis mixture model
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
In this paper, we propose a new vector quantization method to create video thumbnail. In particular, we employ video time density function (VTDF) to explore the temporal characteristics of video data first. A VTDF-based temporal quantization is then applied to segment the whole video in time domain. The optimal number of segments is obtained by a temporal mean square error (TMSE)-based criterion. We employ independent component analysis (ICA) to each temporal segment for feature extraction and build a compact 2D feature space. An ICA mixture-based vector quantization method is developed to explore the spatial characteristics of video data. The optimal number of ICA mixture components is determined by Bayes information criterion (BIC). The video frames that are the nearest neighbors to the quantization codebook are sampled to generate the video thumbnails. Experimental results show that our method is computationally efficient and practically effective to create video thumbnails.
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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 | 0.001 |
| Open science | 0.000 | 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