大规模历史水印识别：数据集和一种新的基于一致性的方法
Large-Scale Historical Watermark Recognition: dataset and a new consistency-based approach
摘要：
历史水印识别是档案工作者和历史学家面临的一个非常实用但尚未解决的挑战。由于大量定义良好的类、杂乱和噪声样本、不同类型的表示、类之间的细微差异和类内的高变化，历史水印对于模式识别也是具有挑战性的。在本文中，克服了数据汇集的困难，我们提出了一个大型的公共数据集，超过6 k的新照片，允许第一次在规模上解决的情况下，学者的实际利益：一次实例识别和跨域的一次实例识别超过16 k细粒度类。我们证明了这个新的数据集足够大，可以训练现代深度学习方法，并表明标准方法可以通过使用中级深度特征来大大改进。更准确地说，我们设计了一个匹配分数和功能微调策略的基础上过滤本地匹配使用空间一致性。与强基线相比，这种基于一致性的方法提供了重要的性能提升。我们的模型在非常具有挑战性的16，753类单次跨域识别任务中实现了55%的top-1准确率，每个类都由经典Briquet目录中的一张图纸描述。除了水印分类，我们展示了我们的方法提供了很好的结果细粒度草图为基础的图像检索。
英文摘要：
Historical watermark recognition is a highly practical, yet unsolved challenge for archivists and historians. With a large number of well-defined classes, cluttered and noisy samples, different types of representations, both subtle differences between classes and high intra-class variation, historical watermarks are also challenging for pattern recognition. In this paper, overcoming the difficulty of data collection, we present a large public dataset with more than 6k new photographs, allowing for the first time to tackle at scale the scenarios of practical interest for scholars: one-shot instance recognition and cross-domain one-shot instance recognition amongst more than 16k fine-grained classes. We demonstrate that this new dataset is large enough to train modern deep learning approaches, and show that standard methods can be improved considerably by using mid-level deep features. More precisely, we design both a matching score and a feature fine-tuning strategy based on filtering local matches using spatial consistency. This consistency-based approach provides important performance boost compared to strong baselines. Our model achieves 55% top-1 accuracy on our very challenging 16,753-class one-shot cross-domain recognition task, each class described by a single drawing from the classic Briquet catalog. In addition to watermark classification, we show our approach provides promising results on fine-grained sketch-based image retrieval.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
