Classification of summarized videos using hidden markov models on compressed chromaticity signatures
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
As digital libraries and video databases grow, we need methods to assist us in the synthesis and analysis of digital video.Since the information in video databases can be measured in thousands of gigabytes of uncompressed data, tools for efficient summarizing and indexing of video sequences are indispensable.In this paper, we present a method for effective classification of different types of videos that makes use of video summarization that is the form of a storyboard of keyframes.To produce the summarization, we first generate a universal basis on which to project a video frame that effectively reduces any video to the same lighting conditions.Each frame is represented by a compressed chromaticity signature.We then set out a multi-stage hierarchical clustering method to efficiently summarize a video.Finally we classify TV videos using a trained hidden Markov model on the compressed chromaticity signatures and also temporal features of videos that are represented by their summaries.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.001 |
| 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