Graph-Based Salient Object Detection using Background and Foreground Connectivity Cues
Bibliographic record
Abstract
Salient object detection is an active research topic due to several potential applications in image compression, scene understanding, image retrieval, and so forth. In this paper, a salient object detection method is proposed by leveraging the recent advances in graph signal processing. Since, the image boundary regions generally belong to the image background, a distribution-based boundary contrast map is generated. Also, the graph representation of the image is used to compute the connectivity of the image regions to the image boundary as well as those to their local neighbors and the image foreground. The connectivity maps obtained are fused with the boundary contrast map in order to obtain the image saliency map. Several experiments are conducted to evaluate the performance of the proposed salient object detection method and to compare it with the state-of-the-arts. Results on datasets of images demonstrate that the proposed method achieves superior performance to the state-of-the-art methods in terms of precision, recall, and mean absolute error values.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".