Optimum kernel function design from scale space features for object detection
Bibliographic record
Abstract
Scale-space representation of an image is a significant way to generate features for classification. However, for a specific classification task, the entire scale-space may not be useful; only a part of it is typically effective. Toward this end, we design a data dependent classification kernel function, which is a weighted mixture of kernels defined on individual scales. In order to choose the optimum weights in the mixture kernel function (MKF), we propose an optimization criterion that leads to the minimization of Raleigh quotient in the positive orthant. This optimization is in general a difficult, non-convex, quadratically constrained quadratic programming. Utilizing a property of ratio of functions, we reduce the aforementioned optimization into a novel binary search, which is essentially a series of quadratic programming. As an application we choose a significant detection problem in oil sands mining called large lump detection from videos. Employing support vector classifier with our MKF yields encouraging results on these difficult-to-process images and compares favorably against the kernel alignment method as well as Fisher criterion adopted in.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".