Video dissolve and wipe detection via spatio-temporal images of chromatic histogram differences
Bibliographic record
Abstract
Gradual transitions represent a challenging problem for temporal segmentation of video. Here we present two new features for detecting these. Ngo et al. set out a method for edge detection in spatio-temporal images made out of the central column (or row, or diagonal) of a video. A wipe generates a diagonal edge in such an image. In this paper we make use of all available pixels to generate spatio-temporal images. For each column of the frame (using only the DC values from a video MPEG), we form a 2D histogram based on chromaticity, and then intersect that histogram with that of the previous frame (one or several frames earlier). The result is an image in which cuts and wipes appear as very strong edges, almost 1s in a background of zeroes. Dissolves require another approach; here we extend a color-distance based histogram metric due to Hafner et al. (1995) by applying the method to 2D Cb-Cr histograms and changing the definition so that the the metric displays a near-constant value during a dissolve, and zero elsewhere. We show results on videos that include fast subject motion and camera movements.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".