A new framework of relevance feedback for content-free image retrieval
Bibliographic record
Abstract
Human beings recognize similarity in scene perception based on their available high-level knowledge about the low-level visual features, which is gradually accumulated throughout their entire lives. Once there is not enough knowledge they tend to rely on low-level visual content. Inspired by this observation, we proposed a new framework of relevance feedback for content-free image retrieval to tackle the problem of sample sparseness. The framework is composed of two components, i.e. short-term feedback and long-term feedback. The former refers to an operation of query conversion and/or refinement during a retrieval session by incorporating a content-aware module, while the latter consists of incrementally updating the system model using the accumulated retrieval results since the last system update. 10000 images from 200 categories of the COREL image collection were employed for evaluating the performance of the framework using the criterion of averaged precision as a function of the number of relevance feedback needed. Experimental results demonstrated a human-like behavior of the proposed framework in that while long-term update helps the system accumulate more knowledge, the content-aware short-term relevance feedback further boosts its performance when the amount of knowledge is limited.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".