Optimization Using Artificial Immune Systems Applied To Object Tracking And Segmentation
Bibliographic record
Abstract
This paper proposes the use of an artificial immune systems (AIS) to obtain the values of hyperparameters of networks such as the kernel parameter of the support vector machines (SVM) in object tracking and weighting factor of the loss term in object segmentation. The proposed iterative AIS method is generic to extend to other image processing tasks by formulating a corresponding objective function (fitness). We verify our method on the STRUCK method that uses SVM to track objects. Depending on feature variations between video frames, our AIS approach incorporates a complementary SVM model to select the SVM parameters for the main SVM model, where our AIS stopping criteria are classification accuracy and number of iterations. We then apply our AIS method to find the parameters that simultaneously minimize both false positives and false negatives of the object segmentation method Graph-Cut. Our results show that our AIS approach achieves significant enhancement of Graph-Cut segmentation accuracy and of STRUCK tracking quality.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".