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Pursuing automated classification of historic photographic papers from raking light photomicrographs

Johnson, Richard and Messier, Paul and Sethares, William A. and Klein, Andrew G. and Brown, Christopher and Do, Anh Hoang and Klausmeyer, Philip and Abry, Patrice and Jaffard, Stéphane and Wendt, Herwig and Roux, Stéphane and Pustelnik, Nelly and Van Noord, Nanne and Van Der Maaten, Laurens and Postma, Eric and Coddington, Jim and Daffner, Lee Ann and Murata, Hanako and Wilhelm, Henry and Wood, Sally and Messier, Mark Pursuing automated classification of historic photographic papers from raking light photomicrographs. (2014) Journal of the American Institute for Conservation, 53 (3). 159-170. ISSN 0197-1360

(Document in English)

PDF (Author's version) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader

Official URL: http://dx.doi.org/10.1179/1945233014Y.0000000024


Surface texture is a critical feature in the manufacture, marketing, and use of photographic paper. Raking light reveals texture through a stark rendering of highlights and shadows. Though close-up raking light images effectively document surface features of photographic paper, the sheer number and diversity of textures used for historic papers prohibits efficient visual classification. This work provides evidence that automatic, computer-based classification of texture documented with raking light is feasible by demonstrating an encouraging degree of success sorting a set of 120 images made from samples of historic silver gelatin paper. Using this dataset, four university teams applied different image-processing strategies for automatic feature extraction and degree of similarity quantification.All four approaches successfully detected strong affinities and outliers built into the dataset. The creation and deployment of the algorithms was carried out by the teams without prior knowledge of the distributions of similarities and outliers.These results indicate that automatic classification of silver gelatin photographic paper based on close-up texture images is feasible and should be pursued. To encourage the development of other classification schemes, the 120-sample “training” dataset used in this work is available to other academic researchers at http://www.PaperTextureID.org.

Item Type:Article
Additional Information:Thanks to Taylor and Francis. This papers appears in Journal of the American Institute for Conservation. ISSN 0197-1360 The original PDF is available at: http://www.tandfonline.com/doi/full/10.1179/1945233014Y.0000000024
HAL Id:hal-03464914
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Other partners > Cornell University (USA)
Other partners > Ecole Normale Supérieure de Lyon - ENS de Lyon (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
Other partners > Université Paris Est Créteil Val de Marne - UPEC (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
Other partners > Delft University of Technology - TU Delft (NETHERLANDS)
Other partners > Indiana University - IU (USA)
Other partners > Paul Messier (USA)
Other partners > Santa Clara University - SCU (USA)
Other partners > The Museum of Modern Art - MoMA (USA)
Other partners > Tilburg University (NETHERLANDS)
Other partners > University of Wisconsin - Madison (USA)
Other partners > Wilhem imaging research (USA)
Other partners > Worcester Art Museum - WAM (USA)
Other partners > Worcester Polytechnic Institute - WPI (USA)
Laboratory name:
Deposited On:03 Apr 2017 11:05

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