Similarity measure and learning with gray level aura matrices (GLAM) for texture image retrieval
Bibliographic record
Abstract
We present a new similarity measure for texture images based on the gray level aura matrices (GLAM), originally proposed by Elfadel and Picard for modeling textures. With the new similarity measure, a support vector machine (SVM) is used to learn pattern similarities for texture image retrieval. In our approach, a texture image is first segmented into clusters of gray level sets. Defined based on the aura measures, a normalized aura matrix is calculated between the gray level sets of the image. The similarity between two texture images computed by the distance of their corresponding normalized aura matrices is defined as the aura matrix distance. The smaller the distance, the more similar are the two textures. To enable the learning of similarity for texture image retrieval, an existing SVM method is adapted to our application, but with a different similarity measure function, different texture feature vectors, and a different similarity ranking scheme for the final retrieved images based on the GLAM. We compare our approach experimentally with existing approaches by performing texture image retrieval from the Brodatz database and the Vistex database. The experimental results show that the proposed approach has performance significantly better than existing approaches with an average successful retrieval rate of 99% - 100% vs 89% - 92% using other approaches.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".