Color constancy is important for various applications such as
image segmentation, object recognition and image retrieval
where object color features are extracted invariant to the illumination
conditions. Different color constancy methods have
been proposed. These methods, in general, compute color
constancy based on all image colors. However, not all pixels
contain relevant information for color constancy. Eventually,
biased pixel values may decrease the performance of color
constancy methods.
To this end, in this paper, we propose a method based on
low-level image features using subsets of pixels. Hence, instead
of using the entire pixel set for estimating the illuminant,
only relevant pixels in the image are used. Therefore,
prior segmentation is performed to learn for different image
categories (e.g. open country, street, indoor) which pixel set
(i.e. image parts) is most appropriate for a reliable estimation.
Based on large scale experiments on real-world scenes, it
can be derived that for certain categories, like open country
and street, the estimation is far more accurate using image
parts than when using the entire image.
http://www.science.uva.nl/~gijsenij
www.colorconstancy.com@InProceedings{GijsenijICIP2007,
author = "Gijsenij, A. and Gevers, T.",
title = "Color Constancy Using Image Regions",
booktitle = "IEEE International Conference on Image Processing",
year = "2007",
url = "https://ivi.fnwi.uva.nl/isis/publications/2007/GijsenijICIP2007",
pdf = "https://ivi.fnwi.uva.nl/isis/publications/2007/GijsenijICIP2007/GijsenijICIP2007.pdf",
has_image = 1
}