Generating Classic Mosaics with Graph Cuts
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
Abstract Classic mosaic is an old and durable art form. Generating artificial classic mosaics from digital images is an interesting problem that has attracted attention in recent years. Previous approaches to mosaic generation are largely based on heuristics, and therefore it is harder to analyse, predict and improve their performance. In addition, previous methods have a number of disadvantages, such as requiring that the number of tiles in a mosaic is known a priori, or relying on extensive user interaction, or using heuristics for tile placement that lead to visible artefacts. We propose a classic mosaic generation algorithm that is based on a principled global optimization. Our approach is fully automatic. We design and optimize an objective function that incorporates the desired mosaic properties, such as tile alignment to significant image edges, prohibiting tile overlap, etc. Our optimization method is based on graph cuts, which proved to be a powerful optimization tool in graphics and computer vision. Experimental comparison to previous work demonstrate the advantages of our approach.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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