Bibliographic record
Abstract
In this paper, an image retrieval framework combining content-based and content-free methods is proposed, which employs both short-term relevance feedback (STRF) and long-term relevance feedback (LTRF) as the means of user interaction. The STRF refers to iterative query-specific model learning during a retrieval session, and the LTRF is the estimation of a user history model from the past retrieval results approved by previous users. The framework is formulated based on the Bayes' theorem, in which the results from STRF and LTRF play the roles of refining the likelihood and the a priori information, respectively, and the images are ranked according to the a posteriori probability. Since the estimation of the user history model is based on the principle of collaborative filtering, the system is referred to as a collaborative Bayesian image retrieval (CLBIR) framework. To evaluate the effectiveness of the proposed framework, nearest neighbor CLBIR (NN-CLBIR) and support vector machine active learning CLBIR (SVMAL-CLBIR) were implemented. Experimental results showed the improvement over content-based methods in terms of both accuracy and ranking due to the integration in the proposed framework.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".