Efficient Indexing for Strongly Similar Image Retrieval
Bibliographic record
Abstract
Strongly similar subimages contain different views of the same object. In subimage search, the user selects an image region and the retrieval system attempts to find matching subimages in an image database that are strongly similar. Solutions have been proposed using salient features or 'interest points' that have associated descriptor vectors.However, searching large image databases by exhaustive comparison of interest point descriptors is not feasible. To solve this problem, we propose a novel off-line indexing scheme based on the most significant bits (MSBs) of these descriptors. On-line search uses this index file to limit the search to interest points whose descriptors have the sameMSB value, a process up to three orders of magnitude faster than exhaustive search. It is also incremental, since the index file for a union of a group of images can be created by merging the index files of the individual image groups. The effectiveness of the approach is demonstrated experimentally on a variety of image databases.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".