A Novel Fusion Approach Based on the Global Consistency Criterion to Fusing Multiple Segmentations
Bibliographic record
Abstract
In this paper, we introduce a new fusion model whose objective is to fuse multiple region-based segmentation maps to get a final better segmentation result. The suggested new fusion model is based on an energy function originated from the global consistency error (GCE), a perceptual measure which takes into account the inherent multiscale nature of an image segmentation by measuring the level of refinement existing between two spatial partitions. Combined with a region merging/splitting prior, this new energy-based fusion model of label fields allows to define an interesting penalized likelihood estimation procedure based on the GCE criterion with which the fusion of basic, rapidly-computed segmentation results appears as a relevant alternative compared with other (possibly complex) segmentation techniques proposed in the image segmentation field. The performance of our fusion model was evaluated on the Berkeley dataset including various segmentations given by humans (manual ground truth segmentations). The obtained results clearly demonstrate the efficiency of this fusion model.
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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 | 0.001 | 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 | 0.000 |
| 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".