RTLBP-AN Efficient Local Pattern For Facial Images Retrieval
Bibliographic record
Abstract
This paper introduces a new local descriptor called radial transition local binary pattern (RTLBP) that is designed to extract more discriminative information from images. Unlike existing descriptors that use a 3 × 3 window, RTLBP uses a 5 × 5 pixel window and separates pixels into two scales. At the first scale, pixels are compared to the central pixel to generate binary values, similar to local binary pattern (LBP). At the second scale, pixels at major and non-major directions are compared differently to create two 8-bit patterns. This information is then processed using four primary and secondary directional radial pixels to create the feature descriptor. Finally, histograms are generated for each of the transformed images. The proposed technique outperforms state-of-the-art descriptors on two publicly available databases, demonstrating its effectiveness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".