Bhattacharyya distance‐based irregular pyramid method for image segmentation
Bibliographic record
Abstract
This paper proposes a new unsupervised image segmentation method by using Bhattacharyya distance‐based irregular pyramid, termed as ‘BDIP’ algorithm. The proposed BDIP algorithm obtains a suboptimal labelling solution under the condition that the number of segments is not manually given. It hierarchically builds each level of the irregular pyramid, with the result that the final segments emerge as they are represented by single nodes at certain levels. The BDIP algorithm employs Bhattacharyya distance to estimate the intra‐level similarity at higher pyramidal levels so as to improve the accuracy and robustness to noise. Furthermore, an adaptive neighbour search method is proposed such that the BDIP algorithm can self‐determine the number of segments. This method considers not only the graphic constraint, but also the similarity constraint in the sense that a candidate node is selected as a neighbour of the centre node if there is no boundary evidence between these two nodes. With the pyramidal accumulation, this evaluation is aggregated into the approximately global evidence, based on which the number of segments can be self‐determined. Experimental results have shown that this proposed BDIP algorithm outperforms other benchmark segmentation algorithms in terms of segmentation accuracy, labelling cost and robustness to noise.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".