Evaluation of Wavelet and Gray Level Co-Occurrence Matrix Combination Model for Texture Image Feature Extraction of Various Types of Meat
Bibliographic record
Abstract
The purpose of this research is to evaluate the combination of Wavelet and Gray Level Cooccurrence Matrix (GLCM) methods in extracting texture features of various types of meat, including beef, buffalo, lamb, horse, and pork.This method integrates the advantages of wavelet transform in capturing spatial-frequency features with GLCM's ability to analyse statistical texture patterns.The classification process is carried out using the k-Nearest Neighbors (k-NN) algorithm, and the model accuracy is evaluated using a confusion matrix.The research results show that the combination of Wavelet and GLCM features significantly improves the classification performance, with an average accuracy of 97.2%.Further analysis shows that the integration of these two methods provides better classification results between fresh, frozen, and rotten categories for each type of meat.Although there are some classification errors, the overall results show the reliability and effectiveness of this approach.Further research can explore parameter optimization or the integration of more sophisticated classification algorithms.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".