Authors:
Nikola Banić
and
Sven Lončarić
Affiliation:
Faculty of Electrical Engineering and Computing and University of Zagreb, Croatia
Keyword(s):
Clustering, Color Constancy, Illumination Estimation, Unsupervised Learning, White Balancing.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Image Enhancement and Restoration
;
Image Formation and Preprocessing
;
Image Formation, Acquisition Devices and Sensors
Abstract:
Most digital camera pipelines use color constancy methods to reduce the influence of illumination and camera
sensor on the colors of scene objects. The highest accuracy of color correction is obtained with learning-based
color constancy methods, but they require a significant amount of calibrated training images with known
ground-truth illumination. Such calibration is time consuming, preferably done for each sensor individually,
and therefore a major bottleneck in acquiring high color constancy accuracy. Statistics-based methods do
not require calibrated training images, but they are less accurate. In this paper an unsupervised learning-based
method is proposed that learns its parameter values after approximating the unknown ground-truth illumination
of the training images, thus avoiding calibration. In terms of accuracy the proposed method outperforms all
statistics-based and many state-of-the-art learning-based methods. The results are presented and discussed.