Identification Method of Ancient Ceramics Revision

Ma, Jia; Peng, Yongkang; Cheng, Wenfang; Qiu, Meikang; Nie, Yu

doi:10.1109/cscloud-edgecom52276.2021.00046

Cited by 3 publications

(8 citation statements)

References 13 publications

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“…An initial set of 3064 sherd photographs of sufficient image quality, size, and design representation was collected for accurate classification by expert analysts. ID Dataset ID10 [29] This article uses self-built ancient pottery data training and verifying models for few-shot learning. Through Baidu, they crawled 23401 pieces of ancient pottery and deleted 17777 pieces of ancient pottery.…”

Section: Datasets For Ancient Ceramics Classificationmentioning

confidence: 99%

“…There are two primary approaches to acquiring these datasets. The former consists of photographing the samples at museums in China [22] while the latter employs Baidu to crawl pieces of ancient ceramics [29].…”

Section: Datasets For Ancient Ceramics Classificationmentioning

confidence: 99%

“…This can be done through web crawling techniques, where specific websites or platforms are crawled to extract relevant data. However, it's important to ensure compliance with legal and ethical guidelines when scraping data from online sources [29].…”

Section: Datasets For Ancient Ceramics Classificationmentioning

confidence: 99%

“…The remaining four papers(18.1%) lack specific details regarding the feature extraction (ID3, ID9, ID10, ID11). Instead, they briefly mention that the images undergo preprocessing and place emphasis on the training procedure utilizing machine learning algorithms [24,[28][29][30].…”

Section: Idmentioning

confidence: 99%

“…Images were also converted to grayscale, to remove any effects of sherd discoloration from soiling or weathering, or variations in white balance when photographing the sherds. ID10 [29] The paper applies prediction with the retrained VGG16-GSSVM to prove that in the case of a self-built few-shot data set, the ratio of training to test is 1:3, and it can achieve high accuracy without using data enhancement and other technologies while coping with a multiobject complex scene.…”

Section: Machine Learning Algorithms For the Classification Of Ancien...mentioning

confidence: 99%

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Finding on Machine Learning Approaches for Classification of Ancient Ceramics - A Systematic Literature Review

Ling,

Delnevo,

Mirri

2023

Preprint

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Ceramics appraisal is a hot topic in the field of cultural relic collection, dating back to prehistoric times. Traditionally, there are primarily two types of ceramics appraisal methods, which are experience-based methods and technology-based methods. In practice, both methods would cause high costs and be time-consuming. This paper presents the results of a systematic literature review of 22 empirical studies that used machine and deep learning algorithms to classify and identify ancient ceramics, encompassing data collection processes to build datasets, feature extraction of ancient ceramics images, and the selection of machine learning algorithms. Major findings included that there has been a growing number of research projects on the use of machine and deep learning algorithms for the classification of ancient ceramics.

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Section: Datasets For Ancient Ceramics Classificationmentioning

confidence: 99%

Section: Datasets For Ancient Ceramics Classificationmentioning

confidence: 99%

Section: Datasets For Ancient Ceramics Classificationmentioning

confidence: 99%

Section: Idmentioning

confidence: 99%

Section: Machine Learning Algorithms For the Classification Of Ancien...mentioning

confidence: 99%

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Finding on Machine Learning Approaches for Classification of Ancient Ceramics - A Systematic Literature Review

Ling,

Delnevo,

Mirri

2023

Preprint

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Findings on Machine Learning for Identification of Archaeological Ceramics: A Systematic Literature Review

Ling,

Delnevo,

Salomoni

et al. 2024

IEEE Access

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AI-Assisted Restoration of Yangshao Painted Pottery Using LoRA and Stable Diffusion

Zhang

2024

Heritage

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This study is concerned with the restoration of painted pottery images from the Yangshao period. The objective is to enhance the efficiency and accuracy of the restoration process for complex pottery patterns. Conventional restoration techniques encounter difficulties in accurately and efficiently reconstructing intricate designs. To address this issue, the study proposes an AI-assisted restoration workflow that combines Stable Diffusion models (SD) with Low-Rank Adaptation (LoRA) technology. By training a LoRA model on a dataset of typical Yangshao painted pottery patterns and integrating image inpainting techniques, the accuracy and efficiency of the restoration process are enhanced. The results demonstrate that this method provides an effective restoration tool while maintaining consistency with the original artistic style, supporting the digital preservation of cultural heritage. This approach also offers archaeologists flexible restoration options, promoting the broader application and preservation of cultural heritage.

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Identification Method of Ancient Ceramics Revision

Cited by 3 publications

References 13 publications

Finding on Machine Learning Approaches for Classification of Ancient Ceramics - A Systematic Literature Review

Finding on Machine Learning Approaches for Classification of Ancient Ceramics - A Systematic Literature Review

Findings on Machine Learning for Identification of Archaeological Ceramics: A Systematic Literature Review

AI-Assisted Restoration of Yangshao Painted Pottery Using LoRA and Stable Diffusion

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