Segmentation of Natural Scenes Based on Visual Attention and Gestalt Grouping Laws
Bibliographic record
Abstract
Detection of salient regions in images of natural scenes can be applied as a pre-processing step for computer vision algorithms as image segmentation, content based image retrieval, object recognition or image compression. This paper presents a visual attention method that analyses the input image in multiple scales using stability information of image regions. The saliency maps constructed have the advantage of preserving well-defined boundaries and making a better separation between background and the salient object, without suffering from undesired effects of multi-scale approaches. Furthermore, a segmentation approach that models Gestalt grouping laws is also applied. The experiments using a database containing 1,000 images showed that our saliency maps outperform the results obtained by other seven visual attention algorithms in terms of F-Measure. In addition, our segmentation approach obtained better results if compared to three classic thresholding algorithms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".