Bibliographic record
Abstract
An image mosaic is a rendering of a large target image by arranging a collection of small source images, often in an array, each chosen specifically to fit a particular block of the target image. Most mosaicking methods are simplistic in the sense that they break the target image into regular tiles (e.g., squares or hexagons) and take extreme shortcuts when evaluating the similarity between target tiles and source images. In this paper, we propose an efficient method to obtain higher quality mosaics that incorporate a number of process improvements. The Fast Fourier Transform (FFT) is used to compute a more fine-grained image similarity metric, allowing for optimal colour correction and arbitrarily shaped target tiles. In addition, the framework can find the optimal sub-image within a source image, further improving the quality of the matching. The similarity scores generated by these high-order cost computations are fed into a matching algorithm to find the globally-optimal assignment of source images to target tiles. Experiments show that each improvement, by itself, yields a more accurate mosaic. Combined, the innovations produce very high quality image mosaics, even with only a few hundred source images.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".