A4 Conference proceedings

Deep Metric Learning for Color Differences


Publication Details
Authors: Zolotarev Fedor, Kaarna Arto
Publication year: 2018
Language: English
Related Journal or Series Information: European Workshop On Visual Information Processing
Title of parent publication: Proceedings of the 2018 7th European Workshop on Visual Information Processing (EUVIP)
Journal acronym: EUVIP
ISBN: 978-1-5386-6898-6
eISBN: 978-1-5386-6897-9
ISSN: 2164-974X
eISSN: 2471-8963
JUFO-Level of this publication: 1
Open Access: Not an Open Access publication

Abstract

Numerous attempts have been made to define a color space and a color distance metric that would closely resemble the human color vision. The uniformity has been the main challenge, the human vision system is more sensitive to some colors while less sensitive to others. A distance given by an ideal metric would match the color difference seen by the human vision system. This study attempts to define such a metric utilizing the spectral data and the available information on the distinguishable colors. Deep neural networks are used in metric learning for modeling the color space and the metric. The resulting metric is then tested against the standard CIEDE2000 metric. DNNs are also used to project spectral data onto a new color space. The results indicate that the new color space with the Euclidean metric is more perceptually uniform than the standard LAB color space with the CIEDE2000 metric. The new metric enables better understanding about the human vision system and measuring the color differences.


Last updated on 2019-10-10 at 14:25