Asymmetric Gaussian-Based Statistical Models Using Markov Chain Monte Carlo Techniques for Image Categorization
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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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 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".