Approach for 3D Cultural Relic Classification Based on a Low-Dimensional Descriptor and Unsupervised Learning

Abstract

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.

Automatic Representative View Selection of a 3D Cultural Relic Using Depth Variation Entropy and Depth Distribution Entropy

“…For 3D cultural heritage classification, Hristov et al developed a software system for classifying archaeological artefacts represented by 2D archaeological drawings [ 32 ]. Gao et al transformed the scale-invariant heat kernel signature descriptor into a low-dimensional feature tensor by the Bag-of-Words mechanism to solve the problem of samples in the dataset that lack category labels [ 33 ]. Many other classification methods have also tried to find more representative features to achieve a more precise result [ 34 , 35 , 36 ].…”

Sanxingdui Cultural Relics Recognition Algorithm Based on Hyperspectral Multi-Network Fusion

“…Li et al [23] put forward an adaptive weighted semantic edge detection algorithm for cultural relics. Gao et al [24] proposed a 3D cultural relic classification method based on a low-dimensional description operator and unsupervised learning. Through a digital image processing approach, Zhang et al [25] investigated the cultural relic character recognition methods.…”