Choquet integral-based aggregation of image template matching algorithms
Bibliographic record
Abstract
Template matching algorithms determine the best matching position of a reference image (template) on a larger image (scene) in either complete or incomplete information environment. In this work, our main objective is to devise a fuzzy integral-based aggregation scheme in an attempt to get more accurate and robust matching, by combining the matching decisions of a finite number of image template matching algorithms, Particularly, Choquet integrals associated with fuzzy measures can be used for handling fuzziness due to incomplete image information. In the present work, a fuzzy integral-based aggregated template matching system is developed on the basis of Choquet integral using belief, plausibility, and probability measure, while being interpreted as an optimistic, a pessimistic, and a noninteracting aggregation, respectively. Finally, to show a validation of Choquet integral-based template matching methods, three individual template matching methods (i,e., MOAD-matcher, SOAD-matcher, and SOSD-matcher) are combined using Choquet integral with respect to different fuzzy measures. Then, performance of these aggregated matchers is compared to individual matchers' performance. It is found that in a complementary sense a Choquet integral-based aggregation of template matching methods gives a better performance compared to the performance of the individual methods.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".