Bibliographic record
Abstract
Local feature points have been widely utilized in solving many problems in computer vision, such as robust matching, object detection and classification, due to the fact that they can hold the intrinsic geometric structures of the image content. However, its investigation in the area of image hashing is still limited. In this paper, we propose a novel shape context based image hashing approach using local feature points, by taking advantage of the geometric invariance of local feature points such as SIFT and preserving the intrinsic structure of the image content using shape context. Experimental results clearly show that the proposed hashing is robust to various classic and malicious attacks, due to the virtue of robust salient keypoints detection as well as the shape context feature descriptors. When compared with the current state-of-art block-based image hashing schemes, such as NMF and FJLT hashing, which extract robust features using dimension reduction, experimental results show that the proposed hashing scheme yields better identification performances under geometric attacks such as rotation attacks and brightness changes, and provides comparable performances under classic distortions such as additive noise, blurring and compression.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".