Sublinear information bottleneck based two-stage deep learning approach to genealogy layout recognition - You, Jianing; Wang, Qing (Frontiers in Neuroscience, 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.