Performance of two Multiscale Texture Algorithms in Classifying Silver Gelatin Paper via K-Nearest Neighbors

Kirsten R. Basinet, Andrew G. Klein, Patrice Abry, Stephane Roux, Herwig Wendt, Paul Messier
2018 2018 25th IEEE International Conference on Image Processing (ICIP)  
Open Archive Toulouse Archive Ouverte OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible To cite this version: Basinet, Kirsten ABSTRACT As part of the Historic Photographic Paper Classification Challenge, a multitude of approaches to quantifying paper texture similarity have been developed. These approaches have yielded encouraging results when applied to very controlled datasets containing
more » ... of familiar specimens. In this paper, we report on the k-nearest neighbors classification performance of two multiscale analysis-based texture similarity approaches when applied to a much larger reference collection of silver gelatin photographic papers. The clusters for this data set were derived from a visual sorting experiment conducted by art conservators and paper experts later extended through crowd-sourcing. The results show that these texture similarity approaches, when combined with a simple k-nearest neighbors classification algorithm, yield workable performances with accuracy of up to 69%. We discuss this outcome in the context of available data and the cross-validation procedure used, then provide suggestions for improvement.
doi:10.1109/icip.2018.8451730 dblp:conf/icip/BasinetKARWM18 fatcat:6ir3hqyicvd3fivzjf4mes5wne