A Novel Histogram-Based Fuzzy Clustering Method for Multispectral Image Segmentation
Bibliographic record
Abstract
Fuzzy C-Means (FCM) clustering has been widely used in remote sensing and computer vision. However, when dealing with multispectral images, the conventional FCM regards spectral responses of all bands on each pixel as a feature vector and conducts image clustering by searching cluster centers in a multi-dimensional space. It is rather time-consuming due to the fact that it has to visit each pixel many rounds during the iteration procedure. Besides, it is sensitive to noise, which mainly results from its ignorance spatial information. In order to overcome these problems, a novel histogram-based fuzzy clustering method is presented in this paper. The proposed method clusters each band independently and fuses the results to form the final segmentation map. On each band, a spatial-spectral image is computed previously, and then the histogram of this image is exploited to find the initial clusters, which is followed by a clustering procedure directly performed on the histogram instead of image pixels. The experimental results over remote sensing images show that the proposed method can achieve more accurate results but uses less time.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".