Research on Random Forest-based Pattern Recognition Method for Conservation of Cultural Heritage of Mural Paintings in Tomb Chambers

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.

Publication Details

Journal
Journal of Combinatorial Mathematics and Combinatorial Computing
Published
2025-03-30
DOI
https://doi.org/10.61091/jcmcc127a-499
Primary Topic
Cultural Heritage Materials Analysis
Type
article
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article

Research on Random Forest-based Pattern Recognition Method for Conservation of Cultural Heritage of Mural Paintings in Tomb Chambers

Journal of Combinatorial Mathematics and Combinatorial Computing
Cultural Heritage Materials Analysis
article

Research on Random Forest-based Pattern Recognition Method for Conservation of Cultural Heritage of Mural Paintings in Tomb Chambers

article en

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.

Journal of Combinatorial Mathematics and Combinatorial ComputingVol. 127a
Sustainable cities and communities
Openalex Percentile: Top 89%
Cultural Heritage Materials Analysis
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Research on Random Forest-based Pattern Recognition Method for Conservation of Cultural Heritage of Mural Paintings in Tomb Chambers · Journal of Combinatorial Mathematics and Combinatorial Computing (2025) | TGRS Research Map | TGRS