Integrated Foreground Segmentation and Boundary Matting for Live Videos
Bibliographic record
Abstract
The objective of foreground segmentation is to extract the desired foreground object from input videos. Over the years, there have been significant amount of efforts on this topic. Nevertheless, there still lacks a simple yet effective algorithm that can process live videos of objects with fuzzy boundaries (e.g., hair) captured by freely moving cameras. This paper presents an algorithm toward this goal. The key idea is to train and maintain two competing one-class support vector machines at each pixel location, which model local color distributions for both foreground and background, respectively. The usage of two competing local classifiers, as we have advocated, provides higher discriminative power while allowing better handling of ambiguities. By exploiting this proposed machine learning technique, and by addressing both foreground segmentation and boundary matting problems in an integrated manner, our algorithm is shown to be particularly competent at processing a wide range of videos with complex backgrounds from freely moving cameras. This is usually achieved with minimum user interactions. Furthermore, by introducing novel acceleration techniques and by exploiting the parallel structure of the algorithm, near real-time processing speed (14 frames/s without matting and 8 frames/s with matting on a midrange PC & GPU) is achieved for VGA-sized videos.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".