ABSTRACT As an important part of art and culture, ancient murals depict a variety of different artistic images, and these individual images have important research value. For research purposes, it is often important to first determine the type of objects represented in a painting. However, the mural painting environment makes datasets difficult to collect, and long-term exposure leads to underlying features that are not distinct, which makes this task challenging. This study proposes a convolutional neural network model based on the classic AlexNet network model and combines it with feature fusion to automatically classify ancient mural images. Due to the lack of large-scale mural datasets, the model first expands the dataset by applying image enhancement algorithms such as scaling, brightness conversion, noise addition, and flipping; then, it extracts the underlying features (such as fresco edges) shared by the first stage of a dual channel structure. Subsequently, a second-stage deep abstraction is conducted on the features extracted by the first stage using a two-channel network, each of which has a different structure. The obtained characteristics from both channels are merged, and a loss function is constructed to obtain the classification result. This approach improves the model's robustness and feature expression ability. The model achieves an accuracy of 84.24%, a recall rate of 84.15%, and an F1-measure of 84.13% when applied to a constructed mural image dataset. Compared with the AlexNet model and other improved convolutional neural network models, the proposed model improves each evaluation index by approximately 5%, verifying the rationality and effectiveness of the model for automatic mural image classification. The mural classification model proposed in this paper comprehensively considers the influences of network width and depth and can extract rich details from mural images from multiple local channels. An effective classification method could help researchers manage and protect mural images in an orderly fashion and quickly and effectively search for target images in a digital mural library based on a specified image category, aiding mural condition monitoring and restoration efforts as well as archaeological and art historical research.

中文翻译：

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基于改进AlexNet网络的古代壁画分类方法

摘要 作为艺术文化的重要组成部分，古代壁画描绘了各种不同的艺术形象，这些个性形象具有重要的研究价值。出于研究目的，首先确定绘画中所代表的对象类型通常很重要。然而，壁画环境使得数据集难以收集，长期暴露导致底层特征不明显，这使得这项任务具有挑战性。本研究在经典AlexNet网络模型的基础上提出了卷积神经网络模型，并结合特征融合对古代壁画图像进行自动分类。由于缺乏大规模壁画数据集，该模型首先通过应用缩放、亮度转换、噪声添加和翻转等图像增强算法来扩展数据集；然后，它提取双通道结构的第一阶段共享的底层特征（例如壁画边缘）。随后，使用双通道网络对第一阶段提取的特征进行第二阶段深度抽象，每个网络具有不同的结构。合并从两个通道得到的特征，构造损失函数得到分类结果。这种方法提高了模型的鲁棒性和特征表达能力。当应用于构建的壁画图像数据集时，该模型实现了 84.24% 的准确率、84.15% 的召回率和 84.13% 的 F1-measure。与AlexNet模型和其他改进的卷积神经网络模型相比，所提出的模型提高了每个评估指标约5%，验证了该模型用于壁画图像自动分类的合理性和有效性。本文提出的壁画分类模型综合考虑了网络宽度和深度的影响，可以从多个局部通道的壁画图像中提取丰富的细节。一种有效的分类方法可以帮助研究人员有序地管理和保护壁画图像，并根据指定的图像类别在数字壁画库中快速有效地搜索目标图像，有助于壁画状态监测和修复工作以及考古和艺术史研究。研究。本文提出的壁画分类模型综合考虑了网络宽度和深度的影响，可以从多个局部通道的壁画图像中提取丰富的细节。一种有效的分类方法可以帮助研究人员有序地管理和保护壁画图像，并根据指定的图像类别在数字壁画库中快速有效地搜索目标图像，有助于壁画状态监测和修复工作以及考古和艺术史研究。研究。本文提出的壁画分类模型综合考虑了网络宽度和深度的影响，可以从多个局部通道的壁画图像中提取丰富的细节。一种有效的分类方法可以帮助研究人员有序地管理和保护壁画图像，并根据指定的图像类别在数字壁画库中快速有效地搜索目标图像，有助于壁画状态监测和修复工作以及考古和艺术史研究。研究。