PURSUING AUTOMATED CLASSIFICATION OF HISTORIC PHOTOGRAPHIC PAPERS FROM RAKING LIGHT IMAGES. Issue 3 (August 2014)
- Record Type:
- Journal Article
- Title:
- PURSUING AUTOMATED CLASSIFICATION OF HISTORIC PHOTOGRAPHIC PAPERS FROM RAKING LIGHT IMAGES. Issue 3 (August 2014)
- Main Title:
- PURSUING AUTOMATED CLASSIFICATION OF HISTORIC PHOTOGRAPHIC PAPERS FROM RAKING LIGHT IMAGES
- Authors:
- Johnson, C. Richard
Messier, Paul
Sethares, William A.
Klein, Andrew G.
Brown, Christopher
Do, Anh Hoang
Klausmeyer, Philip
Abry, Patrice
Jaffard, Stephane
Wendt, Herwig
Roux, Stephane
Pustelnik, Nelly
Van Noord, Nanne
Van Der Maaten, Laurens
Postma, Eric
Coddington, James
Daffner, Lee Ann
Murata, Hanako
Wilhelm, Henry
Wood, Sally
Messier, Mark - Abstract:
- Abstract : 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 athttp://www.PaperTextureID.org .
- Is Part Of:
- Journal of the American Institute for Conservation. Volume 53:Issue 3(2014:Sep.)
- Journal:
- Journal of the American Institute for Conservation
- Issue:
- Volume 53:Issue 3(2014:Sep.)
- Issue Display:
- Volume 53, Issue 3 (2014)
- Year:
- 2014
- Volume:
- 53
- Issue:
- 3
- Issue Sort Value:
- 2014-0053-0003-0000
- Page Start:
- 159
- Page End:
- 170
- Publication Date:
- 2014-08
- Subjects:
- Photographic paper -- Texture -- Eigentexture -- Random-feature texton -- Area-scale analysis -- Anisotropic wavelet multiscale analysis -- Automatic classification -- Texture dataset -- Computational art history -- Digital humanities -- Art authentication -- Image processing for art investigation
Art -- Conservation and restoration -- Periodicals
Museum conservation methods -- Periodicals
Paper -- Preservation -- Periodicals
069.53 - Journal URLs:
- http://aic.stanford.edu/jaic/index.html ↗
http://aic.stanford.edu/jaic/tocvol.html ↗
http://www.ingentaconnect.com/content/maney/jac ↗
http://www.jstor.org/journals/01971360.html ↗
http://www.tandfonline.com/toc/yjac20/current ↗
http://www.tandfonline.com/ ↗ - DOI:
- 10.1179/1945233014Y.0000000024 ↗
- Languages:
- English
- ISSNs:
- 0197-1360
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4686.640000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 11562.xml