Probabilistic winner-take-all segmentation of images with application to ship detection
Bibliographic record
Abstract
A recent neural clustering scheme called "probabilistic winner-take-all (PWTA)" is applied to image segmentation. It is demonstrated that PWTA avoids underutilization of clusters by adapting the form of the cluster-conditional probability density function as clustering proceeds. A modification to PWTA is introduced so as to explicitly utilize the spatial continuity of image regions and thus improve the PWTA segmentation performance. The effectiveness of PWTA is then demonstrated through the segmentation of airborne synthetic aperture radar (SAR) images of ocean surfaces so as to detect ship signatures, where an approach is proposed to find a suitable value for the number of clusters required for this application. Results show that PWTA gives high segmentation quality and significantly outperforms four other segmentation techniques, namely, 1) K-means, 2) maximum likelihood (ML), 3) backpropagation network (BPN), and 4) histogram thresholding.
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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 | 0.000 | 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".