IEEE Comput Graph Appl. 2020 May-Jun;40(3):32-44. doi: 10.1109/MCG.2020.2973109. Epub 2020 Feb 20.
The classification of materials of oracle bone is one of the most basic aspects for oracle bone morphology. However, the classification method depending on experts' experience requires long-term learning and accumulation for professional knowledge. This article presents a multiregional convolutional neural network to classify the rubbings of oracle bones. First, we detected the "shield grain" and "tooth grain" on the oracle bone rubbings, then complete the division of multiple areas on an image of oracle bone. Second, the convolutional neural network is used to extract the features of each region and we complete the fusion of multiple local features. Finally, the classification of tortoise shell and animal bone was realized. Utilizing the image of oracle bone provided by experts, we conducted an experiment; the result show our method has better classification accuracy. It has made contributions to the progress of the study of oracle bone morphology.
甲骨文材料的分类是甲骨文形态学最基本的方面之一。然而，基于专家经验的分类方法需要长期学习和积累专业知识。本文提出了一种多区域卷积神经网络来对甲骨文的拓片进行分类。首先，我们检测甲骨文拓片上的“盾纹”和“齿纹”，然后对甲骨文图像进行多个区域的划分。其次，使用卷积神经网络提取每个区域的特征，并完成多个局部特征的融合。最后，实现龟甲和兽骨的分类。利用专家提供的甲骨文图像进行实验，结果表明，我们的方法具有更好的分类精度。这为甲骨文形态学的研究进展做出了贡献。
