Dynamic Fuzzy Clustering and Its Application in Motion Segmentation
Why this work is in the frame
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Bibliographic record
Abstract
Dynamic textures are common in natural scenes and have recently received great attention in video content analysis. A dynamic fuzzy clustering to automatically segment time-varying characteristics and phenomena is presented in this paper. First, compared with the existing models that assume a common prior distribution, which independently generates the labels, the prior distribution in our model is different for each observation and depends on the labels. In addition, in order to properly account for the neighboring observations during the learning step, we introduce the explicit assumptions of the hidden Markov random field model into the dynamic fuzzy clustering. Second, in order to model the observed dynamic texture data, only grayscale information is taken into consideration of the existing models. We use different visual properties by proposing a new distribution in this paper. Finally, to estimate the model parameters, the gradient method is adopted to minimize the fuzzy objective function with the Kullback–Leibler divergence information. Numerical experiments are presented, where the proposed model is tested on various simulated and real dynamic textures.
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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 | 0.000 | 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 it