Gao Hongjuan, Geng Guohua, Zeng Sheng
School of Information Science & Technology, Northwest University, Xi'an 710127, China.
Xinhua College, Ningxia University, Yinchuan 750021, China.
Entropy (Basel). 2020 Nov 13;22(11):1290. doi: 10.3390/e22111290.
Computer-aided classification serves as the basis of virtual cultural relic management and display. The majority of the existing cultural relic classification methods require labelling of the samples of the dataset; however, in practical applications, there is often a lack of category labels of samples or an uneven distribution of samples of different categories. To solve this problem, we propose a 3D cultural relic classification method based on a low dimensional descriptor and unsupervised learning. First, the scale-invariant heat kernel signature (Si-HKS) was computed. The heat kernel signature denotes the heat flow of any two vertices across a 3D shape and the heat diffusion propagation is governed by the heat equation. Secondly, the Bag-of-Words (BoW) mechanism was utilized to transform the Si-HKS descriptor into a low-dimensional feature tensor, named a SiHKS-BoW descriptor that is related to entropy. Finally, we applied an unsupervised learning algorithm, called MKDSIF-FCM, to conduct the classification task. A dataset consisting of 3D models from 41 Tang tri-color Hu terracotta Eures was utilized to validate the effectiveness of the proposed method. A series of experiments demonstrated that the SiHKS-BoW descriptor along with the MKDSIF-FCM algorithm showed the best classification accuracy, up to 99.41%, which is a solution for an actual case with the absence of category labels and an uneven distribution of different categories of data. The present work promotes the application of virtual reality in digital projects and enriches the content of digital archaeology.
计算机辅助分类是虚拟文物管理与展示的基础。现有的大多数文物分类方法都需要对数据集样本进行标注；然而，在实际应用中，常常缺乏样本的类别标签，或者不同类别的样本分布不均衡。为了解决这个问题，我们提出了一种基于低维描述符和无监督学习的三维文物分类方法。首先，计算尺度不变热核特征（Si-HKS）。热核特征表示三维形状上任意两个顶点之间的热流，热扩散传播由热方程控制。其次，利用词袋（BoW）机制将Si-HKS描述符转换为低维特征张量，即与熵相关的SiHKS-BoW描述符。最后，我们应用一种名为MKDSIF-FCM的无监督学习算法来执行分类任务。使用一个由41件唐三彩胡人陶俑的三维模型组成的数据集来验证所提方法的有效性。一系列实验表明，SiHKS-BoW描述符与MKDSIF-FCM算法显示出最佳的分类准确率，高达99.41%，这为实际中缺乏类别标签以及不同类别数据分布不均衡的情况提供了一种解决方案。当前的工作推动了虚拟现实在数字项目中的应用，并丰富了数字考古的内容。
