The unearthed artifacts from the ruins of the Weiyang Palace in the Han Dynasty’s Chang’an City include a large number of bone stick relics. The front and back of each bone stick form a pair, with similar appearances within each pair, indicating a matching relationship. However, the close resemblance in shape and complex texture features of the bone sticks make traditional image matching methods inefficient and less accurate. To address these challenges, this paper proposes a ConvNeXt-based Siamese network for bone stick image matching, utilizing feature similarity metrics. The method introduces a polarized self-attention module to enhance the model’s focus on the edges and texture features of the bone sticks, thereby improving the feature extraction capability of the ConvNeXt network. The utilization of DIS-Net for background normalization reduces noise interference, enhancing the precision of model matching. The adaptive adjustment of the learning rate using the cosine annealing algorithm accelerates network convergence. Additionally, transfer learning and contrastive learning methods are employed to pre-train the network, expediting convergence and improving matching accuracy. Experimental results demonstrate that compared to Siamese network algorithms with backbone networks such as VGG16, ResNet50, and MobileNet, the proposed method achieves an accuracy of 88.46% and 80% at Rank-10. This indicates a higher matching accuracy, providing an effective solution for automated bone stick matching.
Full Text Finder