Gaussian mixture vector quantization-based video summarization using independent component analysis
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 Gaussian mixture vector quantization (GMVQ)-based method to summarize the video content. In particular, in order to explore the semantic characteristics of video data, we present a new feature extraction method using independent component analysis (ICA) and color histogram difference to build a compact 3D feature space first. A new GMVQ method is then developed to find the optimized quantization codebook. The optimal codebook size is determined by Bayes information criterion (BIC). The video frames that are the nearest-neighbours to the quanta in the GMVQ quantization codebook are sampled to summarize the video content. A kD-tree-based nearest-neighbour search strategy is employed to accelerate the search procedure. Experimental results show that our method is computationally efficient and practically effective to build a content-based video summarization system.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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