A novel video thumbnail extraction method using spatiotemporal vector quantization
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 spatiotemporal vector quantization method to create video thumbnail. In particular, we present a novel video data modeling tools, video time density function (VTDF) to explore the temporal characteristics of video content. A VTDF-based temporal quantization is applied to segment video data in time domain. The optimal number of segments is obtained by a temporal mean square error (TMSE)-based criterion. For each segment, we use independent component analysis (ICA) to build a compact 2D feature space first. A Gaussian mixture-based vector quantization method is then employed to explore the spatial characteristics of each segment. The optimal number of Gaussian components is determined by Bayes information criterion (BIC). The video frames that are the nearest neighbors to the quantization codebook are extracted to abstract the whole segment. Experimental results show that our method is computationally efficient and practically effective to create content-based video thumbnail.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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