An Online Unsupervised Feature Selection and its Application for Background Suppression
Bibliographic record
Abstract
Background suppression in video sequences has recently received great attention. While there exist many algorithms for background suppression, an important and challenging issue arising from these studies concerns that for which attributes of the data should be used for background modelling. It is interesting and difficult because there is no knowledge about the data to guide the search. Also, in real application for background suppression, the video length is unknown and the video frames are generated dynamically in a streaming fashion and arrive one at a time. Thus, it is impractical to wait until all data have been generated before feature learning begins. In this paper, we present an online unsupervised feature selection for background suppression. The advantage of our method is that it avoids any combinatorial search, is intuitively appealing, and allows us to prune the feature set. Moreover, our method, based on the self-adaptive model, has an ability to adapt and change through complex scenes. Experiments on real-world datasets are conducted. The performance of the proposed model is compared to that of other background modelling techniques, demonstrating the robustness and accuracy of our method.
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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".