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Abstract The image classification task of porcelain fragments is of great significance for the digital preservation of cultural heritage. However, common issues are encountered in the image processing of porcelain fragments, including the low computation speed, decreased accuracy due to the uneven distribution of sample categories, and model instability. This study proposes a novel Capsule Network model, referred to as LBCapsNet, which is suitable for the extraction of features from images of porcelain artifacts fragments. A bottleneck-like channel transformation module denoted by ChannelTrans, which resides between the convolutional layer and the PrimaryCaps layer, was first designed. This module is used to reduce the computational complexity and enhance the processing speed when dealing with intricate porcelain images. The MF-R loss function was then proposed by incorporating focal loss into the original loss function. This allows to address the issue of imbalanced distribution of ceramic shard samples and reduce the classification errors, which leads to faster convergence with smoother trend. Finally, an adaptive dynamic routing mechanism is designed with a dynamic learning rate to enhance the overall stability of the classification process. The experimental results obtained on public datasets, such as MNIST, Fashion- MNIST, CIFAR10, FMD and DTD as well as porcelain fragments dataset, demonstrate that LBCapsNet achieves high classification accuracy with faster and more stable computation compared with existing methods. Furthermore, the ability of LBCapsNet to process special textures can provide technical support for the digital preservation and restoration of cultural heritage.

The image classification task of porcelain fragments is of great significance for the digital preservation of cultural heritage. However, common issues are encountered in the image processing of porcelain fragments, including the low computation speed, decreased accuracy due to the uneven distribution of sample categories, and model instability. This study proposes a novel Capsule Network model, referred to as LBCapsNet, which is suitable for the extraction of features from images of porcelain artifacts fragments. A bottleneck-like channel transformation module denoted by ChannelTrans, which resides between the convolutional layer and the PrimaryCaps layer, was first designed. This module is used to reduce the computational complexity and enhance the processing speed when dealing with intricate porcelain images. The MF-R loss function was then proposed by incorporating focal loss into the original loss function. This allows to address the issue of imbalanced distribution of ceramic shard samples and reduce the classification errors, which leads to faster convergence with smoother trend. Finally, an adaptive dynamic routing mechanism is designed with a dynamic learning rate to enhance the overall stability of the classification process. The experimental results obtained on public datasets, such as MNIST, Fashion- MNIST, CIFAR10, FMD and DTD as well as porcelain fragments dataset, demonstrate that LBCapsNet achieves high classification accuracy with faster and more stable computation compared with existing methods. Furthermore, the ability of LBCapsNet to process special textures can provide technical support for the digital preservation and restoration of cultural heritage. [ABSTRACT FROM AUTHOR]