Robust Solvers for Square Jigsaw Puzzles
Bibliographic record
Abstract
A jigsaw puzzle solver reconstructs the original image from a given collection of non-overlapping image fragments using their color and shape information. In this paper we introduce new techniques for solving square jigsaw puzzles (with no prior knowledge of the initial image) that improves the accuracy of the state-of-the-art jigsaw puzzle solvers. While the current puzzle solving techniques are based on finding enhanced compatibility metrics across piece boundaries, we combine the existing techniques to achieve higher accuracy and robustness, i.e., our solver outperforms the known solvers even when the piece boundaries are imprecise. Unlike the most successful puzzle solvers that use greedy pairwise compatibility metrics among puzzle boundaries, we incorporate global information that enhances performance. As a step towards the future goal of developing an automated assembler for real-life corrupted image fragments or shredded documents, we examine puzzles that are corrupted by noise. Our proposed compatibility metrics shows robustness even in such scenarios.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".