A Bayesian approach for texture images classification and retrieval
Bibliographic record
Abstract
Texture analysis plays an important role in many image processing and computer vision tasks, ranging from natural to medical imaging and content-based image retrieval. In this paper, we present an efficient Bayesian algorithm for texture image classification and retrieval, based on Reversible Jump Markov Chain Monte Carlo (RJMCMC) and general Beta mixture models. Our work is motivated by the fact that textured images are generally described by non-Gaussian characteristics which cannot be realistically modeled using rigid distributions. Beta mixtures are able to fit any unknown distributional shape and then can be considered as a useful and flexible solution for the problem of modeling non-Gaussian features present in texture images. In theory, it is well-known that full Bayesian approaches, to handle the mixture estimation and selection problems, are fully optimal. We applied then a fully Bayesian, RJMCMC, technique which simultaneously allows cluster assignments, parameters estimation, and the selection of the optimal number of clusters. Experimental results involving a challenging texture images data set are presented and discussed to show the merits of the proposed work.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".