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Communication Dans Un Congrès Année : 2016

Non-negative dictionary learning for paper watermark similarity

Résumé

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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Dates et versions

hal-01408807 , version 1 (05-12-2016)

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  • HAL Id : hal-01408807 , version 1

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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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