Automatic classification of ceramic sherds with relief motifs

Debroutelle, Teddy; Treuillet, Sylvie; Chetouani, Aladine; Exbrayat, Matthieu; Martin, Lionel; Jesset, Sébastien

doi:10.1117/1.jei.26.2.023010

Cited by 8 publications

(4 citation statements)

References 28 publications

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“…For example, some scholars use the principal component analysis method (PCA) and CNN together. This method is to first use PCA to reconstruct the image, then use CNN to train the character feature image, and finally get a good result (the error rate is controlled within 10%) [ 17 ]. Some researchers use an improved LCNN for handwritten Chinese characters.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Texture Image Classification Method of Porcelain Fragments Based on Convolutional Neural Network

2021

Computational Intelligence and Neuroscience

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The texture image decomposition of porcelain fragments based on convolutional neural network is a functional algorithm based on energy minimization. It maps the image to a suitable space and can effectively decompose the image structure, texture, and noise. This paper conducts a systematic research on image decomposition based on variational method and compressed sensing reconstruction of convolutional neural network. This paper uses the layered variational image decomposition method to decompose the image into structural components and texture components and uses a compressed sensing algorithm based on hybrid basis to reconstruct the structure and texture components with large data. In compressed sensing, to further increase each feature component, the sparseness of tight framework wavelet-based shearlet transform is constructed and combined with wave atoms as a joint sparse dictionary big data. Under the condition of the same sampling rate, this algorithm can retain more image texture details and big data than the algorithm. The production of big data that meets the characteristics of the background text is actually an image-based normalization method. This method is not very sensitive to the relative position, density, spacing, and thickness of the text. A super-resolution model for certain texture features can improve the restoration effect of such texture images. And the dataset extracted by the classification method used in this paper accounts for 20% of the total dataset, and at the same time, the PSNR value of 0.1 is improved on average. Therefore, taking into account the requirements for future big data experimental training, this article mainly uses jpg/csv two standardized database datasets after segmentation. This dataset minimizes the difference between the same type of base text in the same period to lay the foundation for good big data recognition in the future.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Texture Image Classification Method of Porcelain Fragments Based on Convolutional Neural Network

2021

Computational Intelligence and Neuroscience

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“…To ensure a faster pottery classification at a later stage, each pottery is manually labeled at the pottery laser scanning stage, and the source of the labeled information behind the pottery is directly accessible when extracting the pottery 3D model. Researchers in the literature [ 23 ] were inspired by the pyramid histogram and used SVM models to classify the visual features of pottery scans. Since there is a lot of room for optimization of machine learning methods in terms of accuracy and speed.…”

Section: Related Workmentioning

confidence: 99%

Mask R-CNN-Oriented Pottery Display and Identification System

Wei

2022

Computational Intelligence and Neuroscience

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Traditional pottery identification methods are time consuming and costly. In order to cater to more pottery industry needs, we propose a Mask R-CNN-based pottery identification method to build an automatic pottery identification system. We first improve the loss function of Mask R-CNN by using generalized intersection over union loss function, through the pattern of migration learning, to compensate for the disadvantage of a small collective amount of pottery data. For different types of pottery, we use the mask algorithm to enhance the features of the outer contour of the pottery. In addition, we use the minimum external matrix algorithm to accurately extract the outer contour bit pose features of pottery to improve the model’s accuracy in recognizing the outer contour of pottery. To meet the testing conditions of pottery, with the support of potters and archaeologists, we make our pottery data set according to pottery categories. The experimental results prove that our method performs best in the comprehensive recognition accuracy of pottery, with the recognition accuracy above 90%. The recognition accuracy is also the best in pottery color decoration and grain decoration, and the grain recognition accuracy stays above 87%, which is better than other pottery recognition methods.

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“…Binary patterns were automatically extracted from the 3D scans and the classification was done by training a SVM model with pyramid histograms of visual words (PHOW). The recognition rate was below 85% (Debroutelle et al, 2017). Then, we exploited well-known CNN models : AlexNet (Krizhevsky, 2014), VGG11 (Simonyan and Zisserman, 2014) and ResNet18 (He et al, 2015), with a fine tuning on our dataset.…”

Section: Related Workmentioning

confidence: 99%

“…2. For more details, the reader is referred to (Debroutelle et al, 2017). The dataset is composed of 888 grey-level images resized to 224x224 with a black background.…”

Section: Archaeological Materialsmentioning

confidence: 99%