Automated Colorization of a Grayscale Image With Seed Points Propagation
Bibliographic record
Abstract
In this paper, we propose a fully automatic image colorization method for grayscale images using neural network and optimization. For a determined training set including the gray images and its corresponding color images, our method segments grayscale images into superpixels and then extracts features of particular points of interest in each superpixel. The obtained features and their RGB values are given as input for, the training colorization neural network of each pixel. To achieve a better image colorization effect in shorter running time, our method further propagates the resulting color points to neighboring pixels for improved colorization results. In the propagation of color, we present a cost function to formalize the premise that neighboring pixels should have the maximum positive similarity of intensities and colors; we then propose our solution to solving the optimization problem. At last, a guided image filter is employed to refine the colorized image. Experiments on a wide variety of images show that the proposed algorithms can achieve superior performance over the state-of-the-art algorithms.
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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 | 0.001 |
| 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 | 0.001 |
| 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".