Sublinear information bottleneck based two-stage deep learning approach to genealogy layout recognition

Frontiers in Neuroscience

Neuroscience

Jianing You¹  Qing Wang² 

[1] College of Information and Electrical Engineering, China Agricultural University, Beijing, China;null;

关键词: genealogy layout recognition;    sublinear information bottleneck;    YOLOv5 detector;    ResNet;    deep learning;   

DOI  :  10.3389/fnins.2023.1230786

 received in 2023-05-29, accepted in 2023-06-15,  发布年份 2023

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

Unknown   
Copyright © 2023 You and Wang.