Image segmentation using a hierarchical student's‐ <i>t</i> mixture model
Bibliographic record
Abstract
As a significant tool, finite mixture models (FMMs) have been widely used for image segmentation. However, there are two problems with standard FMMs: first, the conditional probability is sensitive to outliers. Second, the robustness to image noise is inadequate. In this study, the authors present a novel hierarchical Student's‐ t MM (HSMM), which includes standard FMMs as a sub‐problem. Additionally, to incorporate more image spatial information, they apply a mean template not only to the prior/posterior probability, but also to the sub‐conditional distribution. Thus, their HSMM is more robust to outliers and image noise owing to the spatial constraints from the mean template. In the standard SMM, a t ‐distribution is used to calculate the conditional probability. In this study, the authors present a novel hierarchical student's‐ t mixture model (HSMM), which includes the standard FMM as a sub‐problem. Finally, though they use Student's‐ t ‐distribution to solve the image segment problems of this study, their HSMM achieves excellent performance, is elastic and can encompass any other model that is based on FMMs. Experimental results demonstrate that their proposed method is robust and effective.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".