Texture segmentation in natural images: Contribution of higher-order image statistics to psychophysical performance
Bibliographic record
Abstract
Perceptual segmentation of a boundary between two textures is conventionally thought to be based upon differences in their Fourier energy, i.e. in their low-order texture statistics. Most evidence supporting (or contradicting) this idea has arisen from studies using various synthetic texture patterns. But what role, if any, do higher-order texture statistics play in segmenting natural images? Here we extracted high resolution texture regions from monochrome photographs of natural scenes, rich in higher-order statistics. By phase-scrambling these textures, we could remove their high-order statistics, leaving mean luminance and RMS contrast unchanged. Using pairs of natural or phase-scrambled textures, we created RMS-balanced texture quilt boundaries in half-disc stimuli. We also created similar contrast boundaries from individual textures. Employing forced choice judgments of boundary orientation (left- vs. right-oblique), we measured modulation-depth thresholds for both contrast and texture boundaries. If only the low-order statistics are used, then phase-scrambling should have no effect on psychophysical performance. Boundaries between these texture pairs could usually be segregated (thresholds: 35–70%), though in some cases even 100% modulation-depth did not produce reliable performance. In most instances, phase-scrambling made the task impossible. However in a minority of scrambled texture pairs, thresholds were measurable and in some, performance was improved. Contrast boundaries yielded lower modulation-depth thresholds (10–30%) which were impervious to or improved by phase-scrambling, particularly at lower texture contrasts. These results suggest that higher-order texture statistics contribute importantly to boundary segmentation in natural scenes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".