Bibliographic record
Abstract
For most color spaces, there is at least one measure for determining the saturation of a color. It is unclear, how well these different measures correspond to human perception. We conducted two experiments in an attempt to fill this gap. We chose 80 color images of natural scenes from the categories "flowers", "man-made", "foliage", and "land-water" from the McGill database of calibrated color images. The images were shown to 8 participants in full color and to another 8 participants in grayscale on a calibrated LCD monitor in randomized order. Participants were asked to select the pixel in the image that appeared to be the most saturated with a mouse cursor. We compared the judgments of the participants to different measures of saturation defined in the DKL, LAB, LUV, and xyY color spaces. We also used saturation from the HSV color space and a measure defined by Koenderink. Our results show that all of the measures capture saturation quite well. The pixels chosen by the participants from the color images were amongst the top 20% saturated pixels for all of the measures, and amongst the top 10% when a small degree of spatial uncertainty with respect to the chosen pixel was allowed. When confronted with the grayscale images, participants were still able to pick pixels whose counterparts in the color images were rated as more saturated by the six measures than randomly selected pixels. Our results indicate that saturation in natural scenes can be specified quite well even without taking image structure into account. Participants are able to infer saturation from the grayscale images from features correlated with color saturation or using prior knowledge in order to make their judgments. Meeting abstract presented at VSS 2014
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".