An evaluation criterion of saliency models for video seam carving
Bibliographic record
Abstract
Modeling human attention has been arousing a lot of interest due to its numerous applications. The process that allows us to focus on some more important stimuli is defined as the “attention”. Seam carving is an approach to resize images or video sequences while preserving the semantic content. To define what is important, gradient was first used but due to limitations of this approach, saliency models, which are able to predict whether regions in the images attract human attention, are now used. Most of them are optimized to conform a ground truth like eye tracking but there is no way to know the efficiency of the saliency models applied in a specific application like in seam carving. In this paper, we propose a criterion, based on the quantity of geometric deformation and the image's reduction, which evaluate the quality of a resizing by seam carving. This criterion is applied on evaluation of image (SCES) or video (SCED) resizing. We validate our criterion with subjective evaluation and used it to rank state of the art saliency models for seam carving. Evaluation of the image by SCES gives a Spearman correlation of −0.92196 and a Pearson correlation of −0.8812. For the video, the final SCED gives a Spearman correlation of −0.81351 and a Pearson correlation of −0.80581.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".