A Siamese Network-based Approach For Matching Various Sizes Of Excavated Wooden Fragments

This paper presents an approach for matching various sizes of excavated wooden fragments based on siamese neural networks. We propose a siamese network composed of global average pooling and a fine-tuned Resnet encoder (GA-S-net). We also propose an elaborated siamese network by replacing the global average pooling with a spatial pyramid pooling layer and add a new dense absolute difference layer (SP-S-net). Samples of 37,760 fragments were prepared from 268 complete wooden tablets excavated from the Heijo-Kyo Palace ruins used during the Nara period in Japan. Both of the networks answer whether two fragments are from the same tablet or not. The result of both networks for the testing set is similar to AUC (Area under the curve) of ROC (Receiver Operating Characteristic) curve being around 90%. In AUC of large fragments, however, SP-S-net is better than GA-S-net with 97.1% versus 93.8%. These networks are rather new for dealing with various sizes of inputs for the matching problem.