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Non-negative dictionary learning for paper watermark similarity

Abstract : In this paper, we investigate the retrieval of paper watermark by visual similarity. We propose to perform the visual similarity by encoding small regions of the watermark using a non-negative dictionary optimized on a large collection of watermarks. The local codes are then aggregated into a single vector representing the whole watermark. Experiments are carried out on a test of tracings (manual binarization of watermarks).
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https://hal.archives-ouvertes.fr/hal-01408807
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Submitted on : Monday, December 5, 2016 - 2:06:12 PM
Last modification on : Monday, January 25, 2021 - 3:16:04 PM
Long-term archiving on: : Monday, March 20, 2017 - 6:49:50 PM

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2016120192940_846503_1168.pdf
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David Picard, Thomas Henn, Georg Dietz. Non-negative dictionary learning for paper watermark similarity. Asilomar Conference on Signals, Systems, and Computers, Nov 2016, Pacific Grove, United States. ⟨hal-01408807⟩

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