Spatial image segmentation based on Beta-Liouville mixture models and Markov Random Field
Bibliographic record
Abstract
Finite mixture models are one of the most widely used probabilistic methods for image segmentation. In this paper, we propose and investigate a mixture model based on Beta-Liouville distributions, which offers more flexibility than previously proposed models. The proposed approach is based on integration of mixture models with Markov Random Field (MRF) with a novel factor that is induced to reduce noise and illumination in images. The model is learned using Expectation Maximization (EM) algorithm based on Newton-Raphson approach. The proposed approach is compared with mixtures of Gaussian, Dirichlet and generalized Dirichlet distributions with integrated MRF. The experimental results demonstrate that proposed segmentation framework gives better performance and better results as compared to mixtures of Gaussian, Dirichlet and generalized Dirichlet with MRF.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".