1 research outputs found
Unsupervised Learning for Color Constancy
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 learning-based
methods. An extension of the method is also proposed, which learns the needed
parameters from non-calibrated images taken with one sensor and which can then
be successfully applied to images taken with another sensor. This effectively
enables inter-camera unsupervised learning for color constancy. Additionally, a
new high quality color constancy benchmark dataset with 1707 calibrated images
is created, used for testing, and made publicly available. The results are
presented and discussed. The source code and the dataset are available at
http://www.fer.unizg.hr/ipg/resources/color_constancy/.Comment: 15 pages, 16 figure