Asymmetric Gaussian-Based Statistical Models Using Markov Chain Monte Carlo Techniques for Image Categorization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A novel unsupervised Bayesian image categorization framework based on asymmetric Gaussian mixture (AGM) model is proposed and the mixture parameter estimation is achieved by sampling-based reversible jump Markov chain Monte Carlo (RJMCMC) method. Previous researches have reveled that AGM outperforms classic symmetric mixture models (i.e Gaussian mixture model (GMM)) since the model adapts both symmetric and asymmetric datasets yielding better fitting accuracy. Moreover, the introduction of RJMCMC, a hybrid self-adapted sampling-based MCMC implementation, enables model transfer throughout parameter learning process, therefore, automatically converges to the optimal number of categories. In order to better identify visual features from the challenging UIUC sport events dataset, the image representative data is generated by adopting scale-invariant feature transform (SIFT), bag-of-visual-words (BOVW) and probabilistic latent semantic analysis (pLSA) techniques. Eventually, irrelevant and unneeded information will be filtered by feature selection. A comparison between AGM and other popular classifiers is given to discover its merits and the direction of future work is suggested.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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