Video Object Segmentation and Tracking Using Probabilistic Fuzzy C-Means
Bibliographic record
Abstract
Automatic video object segmentation and tracking is a challenging problem. In this paper, we introduce a new systematic method for fully automatic object segmentation and tracking using probabilistic fuzzy c-means and Gibbs random fields. The spatial segmentation is based on probabilistic fuzzy c-means clustering and Gibbs sampling. The obtained segmented mask is then refined by taking into account of motion information. Motion vectors are calculated using block matching method based on phase correlation. The motion features and their spatial relationships are used to associate the segmented regions to form video objects. Temporal tracking is achieved by projecting the blocks in current frame to the next frame. The motion-compensated prediction is carried out directly over membership matrix which is used as the initialization of probabilistic fuzzy c-means clustering for the next frame. Experimental results show that the proposed method can automatically extract and track the video object in cluttered background. The major advantages of the proposed method are its ability to deal with deformable objects and being fully automatic
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".