Bibliographic record
Abstract
Introduction In many natural scenes shadows and shading, which are primarily luminance-defined features, proliferate. Hence one might expect the chromatic layers of natural scenes to consist of relatively fewer and larger uniform regions compared to the luminance layers. In other words we might expect color to be more patchy than luminance. On the other hand it has been shown that the Fourier amplitude spectra of the chromatic and luminance layers of natural scenes have similar slopes, which might be taken to imply that color and luminance is equally patchy. We therefore measured the patchiness of the color and luminance layers of a large number of natural scenes, using a new metric of patchiness defined as the average kurtosis across multiple filter scales. We also correlated the patchiness of the scenes with the slopes of their amplitude spectra. Method 234 images from the McGill calibrated color database (resolution of 960×960 pixels) were used for the analysis. Half of the images were of foliage, the other half of man-made objects. The images were decomposed into luminance, red-green and blue-yellow layers, using the modified version of MacLeod-Boynton color space suggested by Ruderman. The image layers were filtered using isotropic bandpass filters of five sizes. The kurtosis of the filtered images were measured and averaged across filter size. The slopes of the amplitude spectra were also measured. Results The red-green layers were significantly more patchy than the luminance layers, for both foliage (p=0.0003) and man-made (p=0.00005) scenes. Blue-yellow layers were only significantly patchier than luminance layers for man-made images (p=0.02). There was no significant correlation between patchiness and spectral slope for the luminance and red-green layers, but a significant correlation for the blue-yellow layer (0.5). Conclusion The red-green layers of natural scenes are more patchy than the luminance layers, and the difference appears unrelated to the slope of the amplitude spectrum.
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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 | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".