Spatiochromatic statistics of natural scenes: First- and second-order information and their correlational structure
Bibliographic record
Abstract
Few studies have investigated the structural relationships between modeled neural images of the luminance, red-green and blue-yellow post-receptoral channels in response to natural scenes. Here we examine these relationships for both first-order, i.e. luminance and color, and second-order, i.e. texture and contrast, variations in a set of natural color images. Images collected using a calibrated digital camera were transformed into LMS cone responses for each pixel, which were then converted into luminance, red-green, and blue-yellow channel images. Simulated responses of cortical first- and second-order operators were produced by convolution with linear filters (Gabor functions) or filter-rectify-filter operators, respectively, for a wide range of filter orientations and spatial frequencies. Filter response amplitudes and image statistics (kurtosis and entropy) were examined, as well as ‘signed’ and ‘unsigned’ cross-correlations between the three first-order channel images and between the first- and second-order channel images. The results demonstrate that first-order red-green has a higher kurtosis/entropy than blue-yellow, which in turn has higher values than luminance. Correlations between first-order luminance and first-order color information are surprisingly high. Additionally, first-order luminance and color are strongly correlated with second-order luminance, but not second-order color. These results suggest that higher-order chromatic statistics play a distinct role in natural images.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".