You Jianing, Wang Qing
College of Information and Electrical Engineering, China Agricultural University, Beijing, China.
Front Neurosci. 2023 Jun 30;17:1230786. doi: 10.3389/fnins.2023.1230786. eCollection 2023.
As an important part of human cultural heritage, the recognition of genealogy layout is of great significance for genealogy research and preservation. This paper proposes a novel method for genealogy layout recognition using our introduced sublinear information bottleneck (SIB) and two-stage deep learning approach. We first proposed an SIB for extracting relevant features from the input image, and then uses the deep learning classifier SIB-ResNet and object detector SIB-YOLOv5 to identify and localize different components of the genealogy layout. The proposed method is evaluated on a dataset of genealogy images and achieves promising results, outperforming existing state-of-the-art methods. This work demonstrates the potential of using information bottleneck and deep learning object detection for genealogy layout recognition, which can have applications in genealogy research and preservation.
作为人类文化遗产的重要组成部分，家谱布局的识别对于家谱研究和保存具有重要意义。本文提出了一种新颖的家谱布局识别方法，该方法使用我们引入的次线性信息瓶颈（SIB）和两阶段深度学习方法。我们首先提出了一种SIB，用于从输入图像中提取相关特征，然后使用深度学习分类器SIB-ResNet和目标检测器SIB-YOLOv5来识别和定位家谱布局的不同组件。所提出的方法在一个家谱图像数据集上进行了评估，并取得了有希望的结果，优于现有的最先进方法。这项工作展示了使用信息瓶颈和深度学习目标检测进行家谱布局识别的潜力，这在族谱研究和保存中具有应用价值。
