论文标题
使用图形神经网络重建古代文档
Using Graph Neural Networks to Reconstruct Ancient Documents
论文作者
论文摘要
近年来，机器学习和深度学习方法（例如人工神经网络）因解决自动拼图解决问题而普及。实际上，这些方法能够从图像中提取高级表示形式，然后可以训练以将匹配图像与非匹配图像分开。这些应用与从部分恢复的碎片中重建的古代文档重建问题有许多相似之处。在这项工作中，我们使用成对补丁信息提出了基于图神经网络的解决方案，将标签分配给表示对之间空间关系的边缘。该网络将源和目标补丁之间的关系分类为上，向下，左，右或无。通过为所有边缘这样做，我们的模型输出了一个代表重建建议的新图。最后，我们表明我们的模型不仅能够在边缘级别提供正确的分类，还可以从一组补丁中生成部分或完整的重建图。
In recent years, machine learning and deep learning approaches such as artificial neural networks have gained in popularity for the resolution of automatic puzzle resolution problems. Indeed, these methods are able to extract high-level representations from images, and then can be trained to separate matching image pieces from non-matching ones. These applications have many similarities to the problem of ancient document reconstruction from partially recovered fragments. In this work we present a solution based on a Graph Neural Network, using pairwise patch information to assign labels to edges representing the spatial relationships between pairs. This network classifies the relationship between a source and a target patch as being one of Up, Down, Left, Right or None. By doing so for all edges, our model outputs a new graph representing a reconstruction proposal. Finally, we show that our model is not only able to provide correct classifications at the edge-level, but also to generate partial or full reconstruction graphs from a set of patches.
