Optimization Using Artificial Immune Systems Applied To Object Tracking And Segmentation
Why this work is in the frame
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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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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 | 0.000 |
| 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 it