Generalized Gaussian mixture Conditional Random Field model for image labeling
Bibliographic record
Abstract
This paper proposes new potential functions for Conditional Random Fields (CRF) in image labeling framework based on generalized Gaussian mixture Modeling (GGMM) of the potential functions. Laplacian mixture potential functions have previously been applied to CRF. However, Laplacian potentials fail to capture data characteristics where data fluctuations happen very smoothly; so that they even give rise to induction of atypical results due to erroneous modeling of data. Having an additional shape manipulation parameter, generalized Gaussian mixtures (GGM) can model data characteristics and fluctuations precisely. In this paper, we propose to deploy GGM in the CRF framework to formulate the potential functions. Expectation maximization (EM) technique is used to estimate GGM parameters. Belief propagation and stochastic gradient descent algorithms are utilized for CRF inference and training, respectively. We show that proposed GGM feature functions effectively improve labeling accuracy of nature images in comparison with Laplacian mixtures. Qualitative labeling results show that the proposed framework performs well particularly for labeling simple even backgrounds where the Laplacian counterparts impose irregular outcomes. That is, despite Laplacian mixtures, GGM-based feature functions can correctly model smooth image color and texture variations.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".