A Fuzzy C-Means Based Spatial Pixel and Membership Relationships for Image Segmentation
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, λs, λg, 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".