Semi-automated relevance feedback for distributed content based image retrieval
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Retrieving images according to the semantic meanings is a challenging problem, mainly due to the complexity of mapping semantic meanings to low-level descriptors. Such complexity raises the scalability issue, especially when the database is distributed over multiple servers such as the peer-to-peer network. To address the scalability issue, we present an approach for content-based image retrieval (CBIR) over a distributed peer-to-peer network. The proposed system features: (1) improved retrieval precision; (2) decentralized database for high availability; (3) decentralized processing to utilize the computation resources. On the proposed peer-to-peer retrieval system, we present (1) query node based and (2) agent based approaches for on-demand advanced-feature calculation. Finally, we present the analysis for semi-automated relevance feedback over the peer-to-peer CBIR framework.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it