Proximity measure image based region merging for texture segmentation through Gabor filtering and watershed transform
Bibliographic record
Abstract
In this paper, an unsupervised texture segmentation scheme is proposed, based on region merging which is carried out on the proximity measure image. A bank of Gabor filters are first applied to the texture image to be segmented, and the proximity measure image, as the feature image, is then constructed by fusing all individual proximity measure images in which pixel intensity reflects the statistical similarity of local histograms in a given neighborhood. Based on the feature image, the task of texture segmentation is thus casted as a problem of region merging and edge detection. To efficiently classify different textures, precisely detect and locate the boundaries of distinct textures, the watershed transform is applied to the feature image for initial segmentation. A region merging procedure is then realized to deal with the over-segmentations resulted from the watershed transform, by iteratively grouping adjacent regions. To demonstrate the effectiveness of the proposed scheme, experiments are carried out on both texture composites and real-world images. Results show that the proposed scheme performs well, in terms of both segmentation accuracy and precision in locating boundaries between distinct textures.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".