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Ancient Mural Classification Method Based on Improved AlexNet Network

机译：基于改进AlexNet网络的古壁画分类方法

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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.
机译：作为艺术和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，这些个别图像具有重要的研究价值。为了研究目的，首先确定绘画中所代表的物体类型通常很重要。然而，壁画绘画环境使得数据集难以收集，长期曝光导致潜在的特征，这些特征不明确，这使得这项任务挑战。本研究提出了一种基于经典AlexNet网络模型的卷积神经网络模型，并将其与特征融合相结合，以自动对古壁图图像进行分类。由于缺少大规模的壁画数据集，该模型首先通过应用图像增强算法（如缩放，亮度转换，噪声）和翻转等图像增强算法来扩展数据集;然后，它提取由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，在使用双通道网络中由第一阶段提取的特征上进行第二阶段深抽象，每个都具有不同的结构。合并来自两个通道的所获得的特征，构造损耗功能以获得分类结果。这种方法提高了模型的鲁棒性和特征表达能力。该模型的准确性为84.24％，召回率为84.15％，并且在施加到构造的壁画数据集时为84.13％的F1测量。与AlexNet模型和其他改进的卷积神经网络模型相比，所提出的模型将每个评估指数提高约5％，验证了自动壁画分类模型的合理性和有效性。本文提出的壁画分类模型全面地考虑了网络宽度和深度的影响，并且可以从多个本地通道中提取来自壁图像的丰富细节。有效的分类方法可以帮助研究人员以有序的方式管理和保护壁形象图像，并快速有效地在数字壁画库中搜索基于指定的图像类别，并触及壁饰条件监测和恢复工作以及考古和艺术历史研究。

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