A Fuzzy C-Means Based Spatial Pixel and Membership Relationships for Image Segmentation
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Bibliographic record
Abstract
Fuzzy C-Means (FCM) is a well-known method for image segmentation. However, since the pixels themselves are considered independent of each other, the segmentation result is sensitive to noise. Fuzzy clustering with spatial constraints provides a powerful way to account for the spatial dependencies between the neighboring pixels, in order to reduce the sensitivity of the segmentation result to noise. In this paper, we propose a new FCM algorithm that incorporates the spatial relationship between neighboring pixels. Different from above methods that depend on parameters α, λ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sub> , λ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">g</sub> , or β to keep a balance between sensitivity with respect to noise and the preservation of salient details, the proposed method does not require any such parameters. Moreover, we introduce a new way to incorporate both the spatial pixel relationship and the spatial membership relationship into the algorithm. To estimate parameters that are to minimize the objective function, we propose a method based on the Lagrange technique. The proposed method is tested on synthetic and real world images and the performance is compared with other methods based on FCM models, demonstrating its robustness with respect to noise and accuracy of image segmentation.
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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 it