Attention Retargeting by Color Manipulation in Images
Bibliographic record
Abstract
Attention retargeting in images is a concept in which the content or composition of the image is altered in an effort to guide the viewer's attention to a specific location. In this paper, we propose a method that modifies the color of a selected region in an image to increase its saliency and draw attention towards it. To avoid many of the issues present in existing approaches to attention retargeting, including high computational complexity and unnatural-looking modifications to the images, we make the case for adjusting hue while leaving all remaining color components fixed. By representing the hue as an angle in CIE L*a*b* color space, we may express an adjustment in hue as a rotation in this space. The optimal hue adjustment is the rotation that maximizes the dissimilarity of hue distribution of the selected region relative to its surroundings. We apply our method on a set of natural images and confirm its effectiveness in guiding attention through eye-tracking. The naturalness of the results are evaluated in a separate set of subjective experiments.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".