Badger Identification Using Handcrafted Image Matching with Learned Convolutional Filter
Bibliographic record
Abstract
A new image matching framework is introduced and applied to the challenging task of badger identification by matching facial characteristics of individual badgers. A novel filter design based on a shallow convolutional neural network for prefiltering images to improve the image matching accuracy is presented. Hill climbing, a commonly-used search optimization algorithm, is used to train this shallow and computationally efficient convolutional network to be deployed at the early stage of an image matching pipeline. The contributions of this work include a novel proposed technique for prefiltering the images using a shallow CNN (Convolutional Neural Network) and applying the filter to the fusion of two handcrafted descriptor algorithms, SIFT (Scale-Invariant Feature Transform) and BRISK (Binary Robust Invariant Scalable Keypoint). Our various combination of these two descriptors achieves a higher F-score than the respective baseline algorithms.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".