When Images Work Faster than Words The Integration of Content-Based Image Retrieval with the Northumbria Watermark Archive

Brown, Andrew; Mulholland, Richard; Graham, Margaret E.; Riley, Jon; Vassilev, V.; Eakins, John P.; Furness, Karen

doi:10.1515/rest.2002.187

Cited by 4 publications

(1 citation statement)

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“…Watermark retrieval has been addressed as a use case for Content-Based Image Retrival (CBIR) methods in the late 1990s by Rauber et al [ 23 ] and Belov et al [ 24 ]. In the Northumbria watermarks archive project [ 25 ] a CBIR system originally developed for trademark retrieval has been applied to tracings and radiographies of historical watermarks. The system worked much better on tracings than on radiographies [ 26 ].…”

Section: Related Workmentioning

confidence: 99%

Cross-Depicted Historical Motif Categorization and Retrieval with Deep Learning

et al. 2020

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In this paper, we tackle the problem of categorizing and identifying cross-depicted historical motifs using recent deep learning techniques, with aim of developing a content-based image retrieval system. As cross-depiction, we understand the problem that the same object can be represented (depicted) in various ways. The objects of interest in this research are watermarks, which are crucial for dating manuscripts. For watermarks, cross-depiction arises due to two reasons: (i) there are many similar representations of the same motif, and (ii) there are several ways of capturing the watermarks, i.e., as the watermarks are not visible on a scan or photograph, the watermarks are typically retrieved via hand tracing, rubbing, or special photographic techniques. This leads to different representations of the same (or similar) objects, making it hard for pattern recognition methods to recognize the watermarks. While this is a simple problem for human experts, computer vision techniques have problems generalizing from the various depiction possibilities. In this paper, we present a study where we use deep neural networks for categorization of watermarks with varying levels of detail. The macro-averaged F1-score on an imbalanced 12 category classification task is 88.3 %, the multi-labelling performance (Jaccard Index) on a 622 label task is 79.5 %. To analyze the usefulness of an image-based system for assisting humanities scholars in cataloguing manuscripts, we also measure the performance of similarity matching on expert-crafted test sets of varying sizes (50 and 1000 watermark samples). A significant outcome is that all relevant results belonging to the same super-class are found by our system (Mean Average Precision of 100%), despite the cross-depicted nature of the motifs. This result has not been achieved in the literature so far.

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Section: Related Workmentioning

confidence: 99%