Set mapping induced image perceptual similarity distance
Bibliographic record
Abstract
Perceptual similarity between any two independent images is addressed with a set mapping perspective by first expanding each image A into a set φ(A) of images, and then defining the similarity between any two images A and B as the smallest average distortion d per pixel between any pair of images, one from φ(A) and the other from φ(B). The resulting similarity metric is dubbed the set mapping induced similarity distance (SMID) between A and B and denoted by dφ(A,B). Several examples of the set mapping φ are illustrated; each of them gives a set φ(A) of images, which may contain images perceptually similar to A to certain degree. It is shown that under some mild conditions, dφ(A,B) is indeed a pseudo distance over the set of all images. Analytic formulas for and lower bounds to dφ(A,B) are also developed for some set mappings φ. When compared with other similarity metrics such as those based on histogram-based methods, SIFT, autocorrelogram, etc., the SMID shows better discriminating power on image similarity. Experimental results also show that the SMID is well aligned with human perception for image similarity.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".