Research on Random Forest-based Pattern Recognition Method for Conservation of Cultural Heritage of Mural Paintings in Tomb Chambers
Bibliographic record
Abstract
The development of digital technology has made the use of machine learning algorithms to protect cultural heritage has become a trend.In this paper, based on the random forest algorithm, the conservation model of tomb mural cultural heritage is recognized.The mural paintings in the tomb of Prince Zhanghuai are used as the data source to construct the tomb mural painting dataset, and the images in the dataset are processed, augmented and labeled.The features such as color, texture and shape in the mural images are extracted as one of the input information of the cultural heritage protection model of the tomb murals.Based on the random forest algorithm, a pattern recognition model for the protection of cultural heritage of tomb frescoes is constructed, and the feature vectors obtained from the feature extraction are used to calculate the split points of the decision tree.The classi ication results of multiple decision trees are weighted and averaged to obtain the inal recognition results.The recognition accuracies of this paper's model on the training set, test set and validation set are 99.45%,95.46% and 92.58%, respectively.This is a signi icant improvement over other existing algorithms.Meanwhile, the algorithm consumes signi icantly less time than the ResNet18 deep residual network model before and after data enhancement, and is able to ef iciently accomplish the task of recognizing the protection of cultural heritage of tomb chamber murals.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.001 |
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".