Similarity Ratio Based Adaptive Mahalanobis Distance Algorithm to Generate SAR Superpixels
Bibliographic record
Abstract
Superpixel algorithms are aimed to partition an image into multiple similar sized segments based on similarity and proximity of pixels. In the heterogeneous regions, the boundaries of the objects should adhere well to the superpixels, and in the homogeneous parts, the pixels should be clustered so that compact superpixels are generated. Since speckle noise inherently exists in synthetic aperture radar (SAR) images their segmentation is considerably more difficult. In this article, the first contribution is the use of Mahalanobis distance instead of Euclidian, so that the superpixels can have elongated shapes to fit the complex structure of the real world better. Secondly, this geometric distance term is combined with similarity ratio term, which leads to even better performance on SAR images. Finally, the global constant that determines the relative importance of geometric proximity and pixel intensity similarity terms, whose best value should be chosen for each image, is considered. Instead of a global constant, its value is determined individually for each superpixel pair as a function of the average values of the superpixels. Experimental results with synthetic and real images demonstrate that the proposed approach has better segmentation performance than many of the existing state-of-the-art algorithms.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".