Similarity measures for efficient 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
New similarity measures for comparing two colour histograms are described: the dissimilitude distance DS* and the similarity distance E. The latter is incorporated into the exponentiation part of the Gibbs distribution and the generalised Dirichlet mixture, while the former is compared to five similarity measures: L1, L2 (Euclidean distance), the similarity measure E in addition to Gibbs and Dirichlet distributions integrating E. The proposed measures are implemented into a system called MIRA for an efficient content-based image mining and retrieval. In order to overcome the limitations (and inappropriateness) of some previous information retrieval measures in evaluating the efficiency of an image retrieval process, three variants of a new effectiveness measure are proposed and experimented on an image collection for various similarity measures, including L1 and L2. Experimental results show that retrieval effectiveness is the highest for E + Dirichlet and the lowest for the Euclidean distance. They also illustrate the superiority of our approach towards similarity analysis and retrieval effectiveness computation both in the L* C* H* and CIECAM02 colour spaces.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| 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