The role of category-specific global orientation statistics for scene categorization
Bibliographic record
Abstract
Real-world scenes contain category-specific regularities in global orientations that correlate well with broad descriptions of scene content, such as naturalness and openness. Here we test the role of global orientation statistics for scene categorization behavior, using line drawings and photographs of real-world scenes. To this end, we selectively disrupted global orientation distributions or local edge coherence and briefly presented the modified scenes to observers, who were asked to categorize each image as beach, city street, forest, highway, mountain, or office. In Experiment 1, we disrupted global orientations of line drawings by random image rotation, local edge coherence by random contour-shifting, or both. We found that contour-shifting impaired categorization accuracy significantly more than image rotation. When line drawings were under both manipulations, scene categorization was the least accurate. These findings suggest that contour orientation contributes to accurate scene categorization, although less so than local edge coherence. In Experiment 2, we normalized the spectral amplitude of grayscale photographs either within a category to preserve category-specific global orientation distributions or across all six scene categories to remove them. We found that category-specific mean amplitude significantly improved participants' categorization accuracy. How does the distribution of category-specific global orientation affect representational structure of scene categories? Across the two experiments, we compared error patterns for manipulated images with those for the intact conditions. The results showed that the error patterns for the images with global orientations disrupted (image rotation, amplitude normalization within or across categories) showed significant correlation with error patterns for intact images. On the other hand, when localized edge coherence was disrupted (contour shifting with or without image rotation), error patterns did not match those for intact images. We conclude that category-specific global orientation distribution aids in accurate scene categorization, but that it has no impact on the categorical representations underlying human scene perception. Meeting abstract presented at VSS 2016
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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 | 0.010 |
| 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 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".