Learning-Based multilabel random walks for image segmentation containing translucent overlapped objects
Bibliographic record
Abstract
Supervised image segmentation methods usually start with information extracted from the learning phase to separate an image into non-overlapping regions. We have used user input information or seeds in our previous work to segment partially overlapped translucent regions. However providing a lot of seeds might sometimes be too time consuming that might make the method perform poorly or not work at all. Machine learning algorithms consist of two major phases: learning phase where the information will be generated based on the data, and test phase where the generated information will be used to improve the performance of the method. In previous work user guided labels were used as hard seeds in the RW algorithm. In this paper we extend our previous work to be able to segment multilabel translucent overlapped objects using soft seed information. We first map each segment as a class on a 25D manifold in the learning phase. Then the probability of assigning each of the image pixels to the segments, data term, is obtained by calculating the geodesic distance between the pixels' features and these classes on the manifold. This data term is then used as soft seeds in the RW algorithm instead of user predefined labels. Experimental results on synthetic images show the strength of our proposed method comparing to our previous algorithm with more than 95% segmentation accuracy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".