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Texture classification of photographic papers: improving spectral clustering using filterbanks on graphs

Abstract : From the point of view of graph signal processing, we show that spectral clustering is equivalent to an ideal low-pass filterbank. Building upon previous multiscale community detection ideas [11], and integrating the concept of community cores [8], we propose a data-driven filterbank-based classification method. We apply this method to the texture classification of photographic papers useful to art historians, and we show that it provides a richer and more informative description of the data’s structure in clusters.
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Submitted on : Thursday, May 4, 2017 - 10:56:38 AM
Last modification on : Monday, July 4, 2022 - 10:07:59 AM
Long-term archiving on: : Saturday, August 5, 2017 - 12:49:04 PM

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  • HAL Id : hal-01518069, version 1
  • OATAO : 17033

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Nicolas Tremblay, Stéphane G. Roux, Pierre Borgnat, Patrice Abry, Herwig Wendt, et al.. Texture classification of photographic papers: improving spectral clustering using filterbanks on graphs. 25eme Colloque Groupe de Recherche et d'Etudes du Traitement du Signal et des Images (GRETSI 2015), Sep 2015, Lyon, France. pp. 1-4. ⟨hal-01518069⟩

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