Background subtraction using Gaussian–Bernoulli restricted Boltzmann machine
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The background subtraction is an important technique in computer vision which segments moving objects into video sequences by comparing each new frame with a learned background model. In this work, the authors propose a novel background subtraction method based on Gaussian–Bernoulli restricted Boltzmann machines (GRBMs). The GRBM is different from the ordinary restricted Boltzmann machine (RBM) by using real numbers as inputs, resulting in a constrained mixture of Gaussians, which is one of the most widely used techniques to solve the background subtraction problem. The GRBM makes it easy to learn the variance of pixel values and takes the advantage of the generative model paradigm of the RBM. They present a simple technique to reconstruct the learned background model from a given input frame and to extract the foreground from the background using the variance learned for each pixel. Furthermore, they demonstrate the effectiveness of the proposed technique with extensive experimentation and quantitative evaluation on several commonly used public data sets for background subtraction.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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