Saliency Priority Using Bottom-up Features for Static and Dynamic Scenes Without Cognitive Bias
Bibliographic record
Abstract
A visual attention system includes the procedure of selecting the most interesting areas (known as salient regions) across visual information that humans receive in daily life. It is necessary to understand how different visual cues affect the human visual system to be able to measure the significance (i.e., saliency) of different regions of a frame. To this end, we designed an empirically based study to investigate bottom-up features including color, texture, and motion in video sequences to achieve a ranking system stating the saliency priority. In this work, we introduced a saliency detection model using a Bayesian framework for static scenes and considered the feature combination scenarios for dynamic scenes under conditions in which we had no cognitive bias. First, we modeled our test data as videos in a virtual environment to avoid any cognitive bias. Then, we performed an eye-tracking based experiment using human subjects to determine how colors, textures, motion directions, and motion speeds interact with each other to attract human attention. This work provides a benchmark to specify the most salient stimulus with comprehensive information for both static and dynamic scenes. The main goal of this work is to create the ability to assign a saliency priority for the entirety of an image/video frame rather than simply extracting a salient object/area which is widely performed in the state-of-the-art.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".