Fuzzy aggregation of image features in content-based image retrieval
Bibliographic record
Abstract
The obstacle of generating hybrid queries within the context of content-based image retrieval is still very real. In attempts to overcome this, fuzzy aggregation can be used to combine single, simple index queries into larger, more complex ones. The paper outlines the use of a fuzzy aggregation technique for hybrid querying which has the ability to adjust its behavior according to operator-controlled parameters. The resulting aggregator can be viewed as a feature-adaptive overall similarity measure. We limit the scope of the aggregator to queries involving color content, color coverage, and horizontal/vertical trends, and apply it to a media database comprised of Corel images of fixed size. Preliminary results show promise and illustrate that hybrid queries using the fuzzy aggregator are effective in their ability to retrieve relevant images while suppressing erroneous retrievals when compared to simple, single-feature queries. In addition, the results obtained are at a minimum comparable to multiple-feature queries generated using a weighted mean approach, but exhibiting scalability and greater flexibility in parameter adjustment.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".