Spatial frequency tuning for outdoor scene categorization
Bibliographic record
Abstract
Which spatial frequencies (SFs) are used for the efficient categorization of real-world scenes? Previous results obtained with the SF Bubbles technique have shown that two SF bands are diagnostic for quick and accurate basic-level categorization of indoor scenes — one around 0.50 cycles per degree of visual angle (cpd) and one around 4.67 cpd (Willenbockel, Gosselin, & Võ, VSS 2017). In the present study, we employed the same technique and paradigm to examine SF tuning for outdoor scene categorization with four natural basic-level categories (coasts, fields, forests, and mountains). The base stimulus set comprised 200 typical gray-scale images per category, all matched in luminance. On each trial, observers saw an image filtered using 20 randomly distributed Gaussian "bubbles". Stimuli were presented in randomized order and remained on the screen until response. Observers were asked to press the space bar as soon as they recognized the scene category, and upon stimulus offset, press the respective key for the correct category. Performance feedback was provided. Mean accuracy across observers was 91.32% correct (SD = 3.64), and mean RT was 451 ms (SD = 107). A multiple linear regression on the transformed RTs from the space bar press and the respective SF filters revealed a significant SF band around 2 cycles per image (cpi; 0.33 cpd) and another one around 26 cpi (4.33 cpd). When using the transformed RTs from the category key press as regressor, only the high-SF band attained significance (27 cpi; 4.50 cpd). Interestingly, the significant SFs closely match those found for fast indoor scene categorization. Additional second-order analyses on both studies' data sets indicate that the significant low- and high-SF bands were used conjunctively. Our results show that people rely on a combination of coarse and fine scales for the efficient basic-level categorization of both indoor and outdoor scenes. Meeting abstract presented at VSS 2018
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".