Saliency Segmentation based on Learning and Graph Cut Refinement
Bibliographic record
Abstract
Saliency detection is a well researched problem in computer vision. In previous work, most of the effort is spent on manually devising a saliency measure. Instead we propose a simple algorithm that uses a dataset with manually marked salient objects to learn to de-tect saliency. Building on the recent success of segmentation-based approaches to object detection, our saliency detection is based on image superpixels, as opposed to individual image pixels. Our features are the standard ones often used in vision, i.e. they are based on color, texture, etc. These simple features, properly normalized, surprisingly have a performance superior to the methods with hand-crafted features specifically designed for saliency detection. We refine the initial segmentation returned by the learned classifier by performing binary graph-cut optimization. This refinement step is performed on pixel level to alleviate any potential inaccuracies due to superpixel tesselation. The initial ap-pearance models are updated in an iterative segmentation framework. To insure that the classifier results are not completely ignored during later iterations, we incorporate classi-fier confidences into our graph-cut refinement. Evaluation on the standard datasets shows a significant advantage of our approach over previous work. 1
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".