EM Clustering of Incomplete Data Applied to Motion Segmentation
Bibliographic record
Abstract
Man yc lustering problems in Computer Vision group data points that are the result of statistical estimation and these data points can have a great amount of uncertainty . Motion segmentation by clustering of optical flo wi ss uch an example because very often optical flo wc annot be estimated without significant uncertainty .W ep resent a EM based clustering algorithm for incomplete data and we apply it to the problem of motion segmentation. The input to the algorithm are the velocity likelihoods and the number of clusters. The algorithm is mathematically very elegant because it does not impose any constraints on the velocity likelihood thus multi-modal likelihood is modeled without difficulty .C oupled with a sophisticated correlated image noise model, the algorithm can handle substantial deviations from the intensity constanc ya ssumption. Experiments with real image sequences sho we xcellent results. 1. Intr oduction The process of grouping pixels having similar motion characteristics is called motion segmentation .Ap opular approach for describing motion similarity within a se gment/layer [12] is by their optical flow. T he computation of optical flo wa tap ixel is an under-constrained problem and the classical solutions [2] almost exclusively use constraints from neighboring pixels by assuming one of the several smoothness constraints which usually do not hold on object boundaries. Motion segmentation based on optical flo wi st hus a chicken and egg problem: In order to compute flo wa ccurately ,w en eed to kno wm otion boundaries but locating the motion boundaries amounts to doing segmentation which requires flo wa si nput. Our approach subscribes to the paradigm [4] that does motion segmentation without computing the full optical flo wf irst.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".