Enhancing Compatibility Accuracy in Jigsaw Puzzle Assembly through Multi-Color Space
Bibliographic record
Abstract
Ensuring precise pairwise compatibility is pivotal in jigsaw puzzle solving. This study mainly focuses on determining the neighbors of the jigsaw pieces by calculating their compatibility. The objective is to enhance the true positive neighbors of the jigsaws to reduce the computational overheads. For this, the paper studies the compatibility measures proposed by the researchers utilizing various color spaces, such as RGB, LAB, and HSV. Finally, it proposes a compatibility measure using a weighted average on the multi-color space. The proposed methodology examines the jigsaw pieces in RGB, LAB, and HSV formats and computes the weighted average for all four sides of each pair of square jigsaw pieces. The key idea of our work is to explore a measure that can be applicable to diverse images, such as underwater and fire images. The paper evaluates the performance of the proposed metric on various standard datasets, such as MIT-432, McGill-540, Pomeranz-805, the EUVP dataset for underwater images, and the D-fire dataset for fire images. The experimental evaluation shows that the proposed measure enhances the true positive neighbor selection by 10% for both colored and underwater pictures in comparison to the approaches that use single-color space.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".