A Novel Learning Dictionary for Sparse Coding-Based Key Point Detection
Bibliographic record
Abstract
Recently, a sparse coding-based key point detector (SCK) was proposed. An SCK shows very impressive performance compared with state-of-the-art key point detection methods on different challenging conditions, such as variations in scale, rotation, context, and nonuniform lighting. The rotational-invariant dictionary in the SCK is, however, manually generated using a time-consuming process of selecting a good seed dictionary and combining multiple versions of its rotated atoms. In this work, the process is automated using a novel duplet autoencoder structure, in which the weights between the input and the hidden layers are designed to embed a rotational-invariant dictionary. A set of loss functions is also proposed to enforce the learning process. A novel retinal image registration pipeline that best uses the new detector is also designed with thorough analysis for selection of different technologies. Extensive experiments on four challenging datasets have confirmed that SCK with the learned dictionary achieves state-of-the-art key point detection performance.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".