Paper Info
Title
A Perceptual Comparison of Distance Measures for Color Constancy Algorithms
Abstract
Color constancy is the ability to measure image features independent of the color of the scene illuminant and is an important topic in color and computer vision. As many color constancy algorithms exist, different distance measures are used to compute their accuracy. In general, these distances measures are based on mathematical principles such as the angular error and Euclidean distance. However, it is unknown to what extent these distance measures correlate to human vision.Therefore, in this paper, a taxonomy of different distance measures for color constancy algorithms is presented. The main goal is to analyze the correlation between the observed quality of the output images and the different distance measures for illuminant estimates. The output images are the resulting color corrected images using the illuminant estimates of the color constancy algorithms, and the quality of these images is determined by human observers. Distance measures are analyzed how they mimic differences in color naturalness of images as obtained by humans.Based on the theoretical and experimental results on spectral and real-world data sets, it can be concluded that the perceptual Euclidean distance (PED) with weight-coefficients (wR= 0.26, wG= 0.70, wB= 0.04) finds its roots in human vision and correlates significantly higher than all other distance measures including the angular error and Euclidean distance.
Year
DOI
Venue
2008
10.1007/978-3-540-88682-2_17
ECCV
Keywords
Field
DocType
color constancy,human vision,distance measures,distance measure,color constancy algorithm,illuminant estimate,perceptual comparison,euclidean distance,angular error,output image,color constancy algorithms,perceptual euclidean distance,different distance measure,image features,computer vision
Computer vision,Color constancy,Color space,Color histogram,Algorithm,Color balance,Artificial intelligence,Color normalization,Color difference,Color quantization,Mathematics,Distance measures
Conference
Volume
ISSN
Citations 
5302
0302-9743
12
PageRank 
References 
Authors
0.83
2
3
Name
Order
Citations
PageRank
Arjan Gijsenij179233.96
Theo Gevers22973214.46
Marcel P. Lucassen3253.71