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作者信息 Identifying Chinese Ceramic Genres Based on Image Modal Transfer and Ensemble Learning Author information 文章历史 收稿日期 修回日期 出版日期 2022-10-16 2023-01-20 2023-12-25 发布日期 2024-02-02 Abstract 关键词 / Key words / EndNote Ris (Procite) Bibtex 导出引用 By using the deep learning algorithm, the useful feature points in the ceramic model are judged, geometric analysis and gradient training are added, and finally, the automatic design sample of the ceramic model is output. 而面对复杂多样的陶瓷品类和形态，人机协同的混合标引方法更具应用前景，既能保证标引效率和标引质量，还能减少多人标引的主观因素影响。 近年来，随着深度学习的不断成熟，图像描述（Image Captioning）技术被越来越多地应用到各种非遗文化的研究中。
One of the main problems faced by image big data is how to efficiently automate image classification With the development of ancient commerce and trade, ancient chinese ceramics spread worldwide and were collected by private individuals or museums. The current mainstream approach is to use image recognitio
The proposed model includes the following
The construction of a dataset for ancient ceramic microscopic images, image preprocessing methods based on gamma correction and clahe. [objective] this paper constructs a clique recognition model for chinese ceramic images It aims to automatically classify and recognize the clique of ceramic images and provide technical support for the research and digital protection of ceramic culture. 论文题目：CCI-ClipCap：一种基于Prompt范式的中国陶瓷图像描述模型（CCI-ClipCap: A Chinese Ceramic Image Description Model Based on Prompt Paradigm）
The goal of this work was to establish a reliable celadon classification model based on edxrf, machine learning algorithm and mahalanobis distance The data set for training machine learning models is constructed of 12 components in the ceramic body and glaze, which are measured by edxrf. Due to the delicate and valuable nature of archaeological porcelains, these methods predominantly rely on digital images rather t Abstract as a treasure of chinese culture, ancient ceramics have been sought at home and abroad since ancient times
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