Sélection adaptative de caractéristiques pertinentes et classification hiérarchique des images dans les bases hétérogènes
Bibliographic record
Abstract
Dans les bases hétérogènes, les images appartiennent souvent à différentes classes thématiques et nécessitent une large description permettant leur reconnaissance. Cependant, les caractéristiques utilisées ne sont pas toujours adaptées au contenu de la base d’images considérée. Nous proposons dans cet article une nouvelle approche se basant sur deux originalités, à savoir la sélection adaptative de caractéristiques et la classification multi- modèle intitulée MC-MM. La sélection adaptative permet de ne considérer que les caractéristiques les mieux adaptées au contenu de la base d’images utilisée. La méthode MC- MM assure la reconnaissance des images en se servant hiérarchiquement des caractéristiques sélectionnées. Les résultats expérimentaux obtenus confirment l’efficacité et la robustesse de notre approche.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".