Hedgehog Shape Priors for Multi-Object Segmentation
Bibliographic record
Abstract
Star-convexity prior is popular for interactive single object segmentation due to its simplicity and amenability to binary graph cut optimization. We propose a more general multi-object segmentation approach. Moreover, each object can be constrained by a more descriptive shape prior, "hedgehog". Each hedgehog shape has its surface normals locally constrained by an arbitrary given vector field, e.g. gradient of the user-scribble distance transform. In contrast to star-convexity, the tightness of our normal constraint can be changed giving better control over allowed shapes. For example, looser constraints, i.e. wider cones of allowed normals, give more relaxed hedgehog shapes. On the other hand, the tightest constraint enforces skeleton consistency with the scribbles. In general, hedgehog shapes are more descriptive than a star, which is only a special case corresponding to a radial vector field and weakest tightness. Our approach has significantly more applications than standard single star-convex segmentation, e.g. in medical data we can separate multiple non-star organs with similar appearances and weak edges. Optimization is done by our modified -expansion moves shown to be submodular for multi-hedgehog shapes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".