Bibliographic record
Abstract
The literature is scarce on content-based copy detection and recall (CBCD). Methods on this categories attempt to exploit one or more feature(s) of images such as shape, color, or texture to recall a query image. The performance of these methods is satisfactory but not perfect. In this paper, we present three fast image copy recall algorithms from a database with perfect results. These algorithms are specifically developed to overcome the problems of copies of same image with different amounts of illumination intensities; similar images with trifling difference(s); and a limited image flipping cases in databases. The algorithms are based on spatial signatures that uniquely represent the images in the database as well as the query image to be recalled. We tested our algorithms on a set of 23, 443 images from LIVE, PIE databases, ORL Database of Faces (the AT&T laboratories Cambridge Database), Caltech-UCSD Birds database and our miscellany of images. Simulations results show that in each case, the query image was recalled with perfect accuracy. Finally, as our results show 100% accuracy, it was unnecessary to compare our results with previously published methods.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".