Transfer Learning Based Center-of-mass Positioning Methods for Cultural Relics

• Published in: IEEE access 2024-01, Vol.12, p.1-1
• Main Authors: He, Lin, Wei, Quan, Gong, Mengting, Yang, Xiaofei, Wei, Jianming
• Format: Article
• Language: English
• Subjects: Attentional mechanisms; Center of mass; Center-of-mass localization; Cultural differences; Data models; Datasets; Feature extraction; Image recognition; Mathematical models; Multilayers; Ontologies; Ontology; Parameters; Semi-supervised learning; Task analysis; Training; Transfer learning
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3349017

In order to address the problem of incomplete data set of real seismic impact of cultural relics in collections, we propose a small sample set oriented center-of-mass positioning method to solve the center-of-mass attribute of cultural relics ontology. First, the method combines the cultural relics image data to construct a deep feature fusion cultural relics ontology recognition model, which extracts and learns the cultural relics image features through multi-layer feature extraction, global information perception and ontology fusion recognition, so as to realize the efficient and accurate recognition of the cultural relic's ontology. Then, the parameter synchronous migration learning method is designed to train and initialize the model, and the data migration strategy of semi-supervised learning is used to retrain the model using the public dataset and the cultural relic image data, to achieve the fine-tuning of the model parameters to improve the accuracy of the cultural relic ontology recognition. Finally, the center-of-mass positioning method is designed to integrate the attention mechanism with the quality of relics and other ontological attributes, and the segmentation of the relic's ontology region is achieved by rectangular stereo fitting and the calculation of the center-of-mass points. At the same time, the fusion self-attention mechanism is used to adjust the regional center-of-mass weights to achieve weighted positioning of the center-of-mass position of the cultural relics. The experimental results show that the present method achieves the best Dice, Acc and MIoU metrics in comparison with various classical models, with improvements of 8.3%, 5.1% and 3.4% respectively. The overall center-of-mass offset of cultural relics is less than 2%, which can achieve accurate identification and center-of-mass positioning of the cultural relics body, fully improve the seismic impact dataset of cultural relics, and enhance the ability of preventive protection of cultural relics against earthquakes.