PURSUING AUTOMATED CLASSIFICATION OF HISTORIC PHOTOGRAPHIC PAPERS FROM RAKING LIGHT IMAGES. Issue 3 (August 2014)

• Record Type: Journal Article
• 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
• Is Part Of: Journal of the American Institute for Conservation. Volume 53:Issue 3(2014:Sep.)
• 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
• DOI: 10.1179/1945233014Y.0000000024
• Languages: English
• ISSNs: 0197-1360

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 .