Large-Scale Historical Watermark Recognition: dataset and a new consistency-based approach
| Autor: | Marc H. Smith, Xi Shen, Ilaria Pastrolin, Spyros Gidaris, Oumayma Bounou, Mathieu Aubry, Olivier Poncet |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Matching (statistics) business.industry Computer science Computer Vision and Pattern Recognition (cs.CV) Deep learning Computer Science - Computer Vision and Pattern Recognition 020207 software engineering Watermark Pattern recognition 02 engineering and technology 010501 environmental sciences 01 natural sciences Consistency (database systems) Pattern recognition (psychology) 0202 electrical engineering electronic engineering information engineering Feature (machine learning) Artificial intelligence business Image retrieval Digital watermarking 0105 earth and related environmental sciences |
| Zdroj: | ICPR |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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