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5 CONCLUSIONS
This paper presents 6 normalised color distances,
based on widely used metrics in the RGB and L*a*b*
color models. An adjusted City Block normalised
color distance is proposed for the HSV model. The
color distances were evaluated with 3 experiments.
The first one, focusing on color perception, clearly
indicated that the L*a*b* based distances are much
better than those based on RGB and HSV for the eval-
uation of color similarity (and difference), being more
closely aligned with the human color perception. The
differences between the L*a*b* distances themselves
were found to be negligible, in what regards the color
perception evaluation performed.
The second experiment showed that although the
normalised distances all have values between 0 and
1 potentially, in reality the range of values used is
much smaller. This is particularly noticeable for the
L*a*b* based distances. This fact is irrelevant if the
distance is used in a relative context, but it can be an
issue when the color distance is used as an absolute
measurement. A possible solution could be to remap
the distance, for example with a linear transforma-
tion mapping the 0.1 and 99.9 percentiles (Table 1) to
[0, 1]. This would result in some saturation (of 0.2%
of the elements), which could be reduced by using
more extreme percentile values (e.g. 0.01 and 99.99).
The third experiment was designed to evaluate the
ability of the color distances to compare and identify
the best image match. The test images selected have
some diversity, but they also have a predominant color
and are thus easily matched in color classes by a hu-
man observer. The goal was to verify the effectiveness
of the normalised color distances to perform the same
task. For this experiment, a spatially tolerant color
distance D
(v)
was proposed, to account for a possi-
ble geometric misalignment between two images be-
ing compared, as well as a modified Dunn index for
the evaluation of the results. The modified Dunn in-
dex was found to be an useful tool, allowing for large
number of image (and color) comparisons to be sum-
marised effectively. The spatially tolerant color dis-
tance D
(v)
was found to be slightly better than a com-
parison of images with a direct pixel by pixel pairing.
The L*a*b* based distances proved to be much better
than those based on the HSV and RGB color models
for the comparison of images, with the L*a*b* City
Block distance (d
CB
Lab
) having the best performance.
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