A Dynamic Bayesian Framework for Motion Segmentation
Bibliographic record
Abstract
Dynamic textures are sequences of images of moving scenes in time that are common in natural scenes and play an important role in video content analysis. This paper presents a new dynamic Bayesian framework for segmentation of dynamic textures. First, we formulate the problem in the Bayesian framework using mixture model theory. The major advantage of our approach is that it provides a natural way to cluster data based on the components of the mixture that generated it. Second, in order to model the distribution of observed data, only grayscale information is taken into consideration of the existing mixture models. In order to overcome this problem, a new distribution is presented in this paper. The advantage of the proposed distribution is that it has the flexibility to fit different kinds of observed data and is more reliable for changes of noise and contrast levels. Finally, expectation maximization (EM) algorithm is adopted to maximize the lower bound on the data log-likelihood and to optimize the parameters. The proposed model is successfully compared to the state of the arts dynamic texture segmentation approaches. Numerous experiments are presented where our model is tested on various simulated and natural real-world dynamic textures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".