An advanced computational method to determine co-occurrence probability texture features
Bibliographic record
Abstract
A critical shortcoming of determining co-occurrence probability texture features using Haralick's popular grey level co-occurrence matrix (GLCM) is the excessive computational burden. Here, a more robust algorithm (the grey level cooccurrence integrated algorithm or GLCIA) to perform this task is presented. The GLCIA is created by integrating the preferred aspects of two algorithms: the grey level cooccurrence hybrid structure (GLCHS) and the grey level cooccurrence hybrid histogram (GLCHH). The GLCHS utilizes a dedicated 2-d data structure to quickly generate the probabilities and apply statistics to generate the features. The GLCHH uses a more efficient 1-d data structure to perform the same tasks. Since the GLCHH is faster than the GLCHS yet the GLCHH is not able to calculate features using all available statistics, the integration of these two methods generates a superior algorithm (the GLCIA). The computational gains vary as a function of window size, quantization level, and statistics selected. The GLCIA computational time relative to that of the standard GLCM method ranges from 0.04% to 16%. The GLCIA is a highly recommended technique for anyone wishing to calculate co-occurrence probability texture features, especially from large-scale digital imagery.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".