A signature file algorithm for large image databases
Bibliographic record
Abstract
Signature file, which is an indexing technique, has been extensively studied in text retrieval.It acts as a filtering mechanism which is able to screen out the most non- qualifying documents, thus, confining document searches to smaÌler relevant candidate sets.Many methods for organizing signature flles have been proposed to improve searching speed since querying a large signature file sequentially is very time con- suming.However, these methods have limitations when they are applied to image databases since the distinct characteristics of image databases have not been taken into account.The goal of this research is to design an indexing algorithm for large image databases.This proposed algorithm, called the Image Signature Tlee (1,97), re- trieves an image in a database based on image objects and spatial relations between the objects contained in an image, as weil as image sizes and formats.Signature file technique is adopted in this algorithm.Performance evaluation is conducted both analytically and experimentally.The analybical study of the performance in term of signature reduction ratio was conducted based on probability theory.The retrieval cost, signature reduction ratio, storage cost and update cost are studied by simulation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".