Chaos and MPEG-7 based feature vector for video object classification
Bibliographic record
Abstract
We propose a method to generate unique feature vectors for video objects using chaos theory and MPEG-7 visual descriptors. We consider each feature element of visual descriptors as a dynamic system. The proposed method performs feature binding of the re-constructed trajectory of simulated chaotic attractors using histogram analysis. The binding derives two feature vectors different from the original MPEG-7 one, for each video object. We use the new vectors for object classification. Low (e.g., Logistic Map) and high (e.g., Mackey-Glass) dimensional chaotic attractors are used. Dynamic feature reduction (35.44% on average) in the proposed feature vectors are achieved from the MPEG-7 feature vector. Cross validation accuracy with different classifiers shows significant (87.6% on average) improvement with the proposed feature vectors over that (73.2% on average) of the MPEG-7 feature vector.
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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 | 0.000 |
| 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 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".