Peak Valley Edge Patterns: A New Descriptor for Biomedical Image Indexing and Retrieval
Bibliographic record
Abstract
A new algorithm meant for biomedical image retrieval application is presented in this paper. The local region of image is represented by peak valley edge patterns (PVEP), which are calculated by the first-order derivatives in 0°, 45°, 90° and 135° directions. The PVEP differs from the existing local binary pattern (LBP) in a manner that it extracts the directional edge information based on first-order derivative in an image. Further, the effectiveness of our algorithm is confirmed by combining it with Gabor transform. The performance of the proposed method is tested on VIA/I-ELCAP database which includes region of interest computer tomography (ROI-CT) images. Performance analysis shows that the proposed method improves retrieval results from 79.21% to 86.13% and 51.91% to 55.06% as compared to LBP in terms of average precision when number of top matches considered is 10 and 100 respectively.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".