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Conference papers

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

Nicolas Tremblay 1 Stéphane G. Roux 1 Pierre Borgnat 1 Patrice Abry 2 Herwig Wendt 3, 4 Paul Messier 5 
1 Phys-ENS - Laboratoire de Physique de l'ENS Lyon
2 Paul Messier (USA)
3 IRIT-SC - Signal et Communications
IRIT - Institut de recherche en informatique de Toulouse
4 CNRS - Centre National de la Recherche Scientifique
5 Chercheur indépendant
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.
Keywords : Filterbank Print Spectral clustering Community detection Art historian
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Last modification on : Monday, July 4, 2022 - 10:07:59 AM
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  • HAL Id : hal-01518069, version 1
  • OATAO : 17033

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ENS-LYON | UNIV-TLSE2 | CNRS | UNIV-LYON1 | SMS | UT1-CAPITOLE | UDL | IRIT | IRIT-SC | IRIT-SI | IRIT-CNRS | UNIV-UT3 | UT3-TOULOUSEINP

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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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