Guiding visual attention by manipulating orientation in images
Bibliographic record
Abstract
Visual attention plays an important role in directing our gaze to potentially interesting areas in images. Our attention is involuntarily drawn to areas that are perceptually different from their immediate surroundings. Such areas are labeled “salient.” They originate from variations in principal visual features such as color, intensity, and orientation. In this study, we analyze how manipulating the orientation of a particular region of an image affects human visual attention. Statistical Hough transform is applied on a selected region in an image to construct the edge distribution of that region over a range of orientations. The remainder of the image is analyzed using a weighted statistical Hough transform to obtain the edge distribution in the region's surroundings. We measure the dissimilarity between these two distributions as the region is rotated and show that the region becomes more salient as the dissimilarity is increased. This model also allows us to predict the angle of rotation at which the selected region becomes most salient, which enables us to manipulate the image so that the selected region's saliency is maximized. We apply our method to a set of natural images and verify its effectiveness through eye-tracking.
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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 | 0.000 | 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) | 0.001 | 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".