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PURSUING AUTOMATED CLASSIFICATION OF HISTORIC PHOTOGRAPHIC PAPERS FROM RAKING LIGHT IMAGES
- Source :
- Journal of the American Institute for Conservation. 53:159-170
- Publication Year :
- 2014
- Publisher :
- Informa UK Limited, 2014.
-
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 at http://www.PaperTextureID.org.
- Subjects :
- Information retrieval
business.industry
media_common.quotation_subject
010401 analytical chemistry
Museology
Feature extraction
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Classification scheme
02 engineering and technology
Conservation
Art
01 natural sciences
0104 chemical sciences
Rendering (computer graphics)
Outlier
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Degree of similarity
Computer vision
Artificial intelligence
business
Gelatin silver process
Photographic paper
media_common
Subjects
Details
- ISSN :
- 19452330 and 01971360
- Volume :
- 53
- Database :
- OpenAIRE
- Journal :
- Journal of the American Institute for Conservation
- Accession number :
- edsair.doi...........0a6efd7d89c7f96247f792be34a206bb