A multi-objective approach based on TOPSIS to solve the image segmentation combination problem
Bibliographic record
Abstract
Recently, there has been renewed interest in the fusion of image segmentation. However, previous relevant research has been impeded by the lack of an appropriate single segmentation criterion, which yields an improved final segmentation result. This paper proposes a new framework to tackle this problem. It is based on multi-objective optimization strategy, followed by a decision making technique called: technique for order performance by similarity to ideal solution (TOPSIS). This new fusion framework aims to overcome the limits caused by using a single criterion by combining and optimizing, simultaneously, two different and complementary segmentation criteria; namely, the global consistency error (GCE) (region-based criterion) and the F-measure (edge-based criterion). This new multi-criterion fusion framework is validated on the Berkeley image dataset and compared to different segmentation algorithms (with or without fusion strategy). Experiments show that the results of our new multi-objective approach improve the state of the art in terms of popular indices.
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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 | 0.001 |
| Open science | 0.001 | 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".