1. A New Perceptually Uniform Color Space with Associated
Color Similarity Measure for Content-Based Image and
Video Retrieval
M. Sarifuddin Rokia Missaoui
Departement d’informatique et d’ingenierie,
´ ´ Departement d’informatique et d’ingenierie,
´ ´
Universite du Quebec en Outaouais
´ ´ Universite du Quebec en Outaouais
´ ´
C.P. 1250, Succ. B Gatineau C.P. 1250, Succ. B Gatineau
Quebec - Canada, J8X 3X7
´ Quebec - Canada, J8X 3X7
´
m.sarifuddin@uqo.ca rokia.missaoui@uqo.ca
ABSTRACT visual features like color, shape and texture. Given a large
Color analysis is frequently used in image/video retrieval. range of images such as landscape, satellite, and medical im-
However, many existing color spaces and color distances fail ages, human visual system has the capacity to distinguish,
to correctly capture color differences usually perceived by recognize and interpret different types of objects in images.
the human eye. The objective of this paper is to first high- However, computer programs can hardly recognize image
light the limitations of existing color spaces and similarity objects even in a simple scene. In image processing and
measures in representing human perception of colors, and computer vision, color analysis (e.g., dominant color identifi-
then to propose (i) a new perceptual color space model called cation, color-based object detection) is a low-level operation
HCL, and (ii) an associated color similarity measure denoted which plays an important role in image/video retrieval.
DHCL . Experimental results show that using DHCL on the
new color space HCL leads to a solution very close to hu- A variety of color spaces have been developed for color rep-
man perception of colors and hence to a potentially more resentation such as RGB, perceptual color spaces HSL (hue,
effective content-based image/video retrieval. Moreover, the saturation, luminance), HSV/HSB (hue, saturation, value
application of the similarity measure DHCL to other spaces or brightness) [13, 14] and HSI (hue, saturation, intensity)
like HSV leads to a better retrieval effectiveness. as well as perceptually uniform color spaces like L*u*v*, and
L*a*b* (luminance L*, chrominance u*, v*, a*, and b*) and
A comparison of HCL against L*C*H and CIECAM02 spaces CIECAM02 [7, 15]. We recall that perceptual uniformity in
using color histograms and a similarity distance based on a given color space means that the perceptual similarity of
Dirichlet distribution illustrates the good performance of two colors is measured by the distance between the two color
HCL for a collection of 3500 images of different kinds. points.
The objective of this paper is to first illustrate the limi-
Keywords tations of existing color spaces and similarity measures in
Color spaces, content-based image retrieval, similarity mea-
representing human perception of colors, and then to pro-
sures.
pose (i) a new color space model which aims at capturing
the real color difference as perceived by human eye, and
1. INTRODUCTION (ii) a new color similarity measure. The proposed space is
Challenges in content-based image retrieval (CBIR) consist inspired from HSV (or HSL) and L*a*b*.
not only to bridge the semantic gap (i.e., the mismatch be-
tween the capabilities of CBIR techniques and the semantic The paper is organized as follows. Section 2 is a brief de-
needs of the users) but also to exploit different models of hu- scription of color spaces, their strengths and limitations.
man image perception, and manage large image collections Section 3 presents a new color space called HCL while Sec-
and incomplete query/image specifications [12]. The human tion 4 presents a set of existing color distances, proposes
visual system does not perceive a given image as a mere a new similarity measure and provides a performance anal-
and aleatory collection of colors and pixels, but rather as a ysis of color distances applied to a set of color spaces. A
layout of homogeneous objects and regions with respect to conclusion is given in Section 5.
2. COLOR SPACES
The most commonly used and popular color space is RGB.
However, this space presents some limitations: (i) the pres-
ence of a negative part in the spectra, which does not allow
the representation of certain colors by a superposition of the
three spectra, (ii) the difficulty to determine color features
like the presence or the absence of a given color, and (iii)
the inability of the Euclidean distance to correctly capture

2. color differences in the RGB space. Figure 4 illustrates the
latter fact.
Color spaces like HSV and HSL are also commonly used in
image processing. As opposed to the RGB model, HSL and
HSV are considered as natural representation color models
(i.e., close to the physiological perception of human eye). In
these models, color is decomposed according to physiological
criteria like hue, saturation and luminance. Hue refers to
the pure spectrum colors and corresponds to dominant color
as perceived by a human. Saturation corresponds to the
relative purity or the quantity of white light that is mixed
with hue while luminance refers to the amount of light in a
(a) (b)
color [2].
Figure 1: a) L*a*b* and L*C*H* color space models.
A great advantage of HSL/HSV models over the RGB model
b) Chroma and Luminance variations for six hue values.
lies in their capacity to recognize the presence/absence of
colors in a given image. However, the main drawback of
HSL and HSV models concerns their luminance variation
which does not correspond to human perception. Visually, and purple. One can notice that hue angle for blue varies
a color with a great amount of white has small variation of between 2570 and 2740 .
luminosity than a fully saturated color. Such a situation is
not correctly captured in these models.
In the HSV model, saturated colors have the same intensity
as colors with 100% of white color. However, this is not the
case for the HSL model since there is a great luminosity gap
between saturated colors and colors with a great amount of
white. Therefore, using metric distances such as Euclidean
(see Equation 6) and cylindric distances (see Equation 10)
with HSV and HSL models does not capture the color dif-
ference as human eye does.
The CIE (Commission Internationale de l’Eclairage) has de-
fined two perceptually uniform or approximately-uniform
color spaces L∗ a∗ b∗ and L∗ u∗ v ∗ . Further, the L∗ C ∗ H ∗ (a) (b)
(Lightness, Chroma, and Hue) and L∗ t∗ θ∗ (t = Chroma
and θ∗ = Hue) color spaces have been defined as derivatives Figure 2: a) CIECAM02 color space model. b) Chroma
of L∗ u∗ v ∗ and L∗ a∗ b∗ [3]. The L*a*b* and L*C*H* color and luminance variations for six hue values.
models are represented in Figure 1. Figure 1-a shows color
distribution in these models while Figure 1-b illustrates the
variation of chroma C ∗ et luminance L∗ for six different 3. A NEW COLOR SPACE
hue values H ∗ (red, yellow, green, cyan, blue and purple).
While in [6] we propose new similarity semi-metric distances
One can see that the luminosity of a hue (respectively the
based on color histograms, the present paper investigates
chroma) grows (respectively decreases) slowly according to
color pixel similarity analysis on a new perceptually uni-
the increase in the percentage of white. This variation cor-
form color space that we call HCL (Hue, Chroma and Lu-
responds to human perception and hence represents a good
minance). Such a new color space exploits the advantages
feature in L*a*b* and L*C*H* color models.
of each one of the color spaces: HSL/HSV and L∗ a∗ b∗ and
discards their drawbacks.
As pointed out by [7, 8], the spaces L*a*b* and L*C*H*
have a significant deficiency since they have weak hue con-
We assume that the chroma and the hue of any color can be
stancy for blues as illustrated by Figure 1-a) which shows
defined as a blend of the three chrominance elemental sensa-
that the blue hue angle varies between 2900 to 3060 . Hue
tions: R-G (from red to green), G-B (from green to blue) and
constancy means that a color object created by varying the
B-R (from blue to red). Based on this assumption and the
encoding values to obtain different sensations in lightness or
Munsell color system with the three color attributes closed
chroma should still lead to the same hue over the entire ob-
to human perceptions: hue (H), chroma (C) and luminance
ject. Moreover, simple nonlinear channel editing should not
(L), we define below a mapping from RGB space to HCL
have an impact on the hue of a color. In order to get such
space.
constancy, another color space called “CIE Color appear-
ance model” (CIECAM02) has been proposed in [7]. How-
We recall that a color containing a lot of white is brighter
ever, CIECAM02 improves hue constancy for almost all col-
than one with less white. A saturated color contains 0% of
ors except the blue as illustrated in Figure 2-b which shows
white and has a maximum value of chroma. An increasing
the variation of hue angles for red, yellow, green, cyan, blue
value of white leads to a decreasing value of chroma and

3. a less saturated color. Concretely, a color is saturated if or
M ax(R, G, B) is equal to R, G, or B, and M in(R, G, B) = 0. if ((R − G) ≥ 0 and (G − B) ≥ 0), then H = 2 H
3
The saturation of a color is null (i.e., chroma =0) when if ((R − G) ≥ 0 and (G − B) < 0), then H = 4 H
3
M in(R, G, B) = M ax(R, G, B). Therefore, we will use the if ((R − G) < 0 and (G − B) ≥ 0), then H = 180 + 4 H
3
expressions M ax(R, G.B) and M in(R, G, B) to compute lu-
if ((R − G) < 0 and (G − B) < 0), then H = 3 H − 180.
4
minance L.
(5)
Human vision reacts in a non-linear (logarithmic) manner to
color intensity. For example, a 20% reduction of luminosity
is perceived as a 50% reduction. Based on the proportion-
ality law of Van Kries, luminance L can be expressed by
Q.Y where Y corresponds to the luminosity captured by a
photo-receptor. Color spaces YIQ, YUV, YCrCb, L*u*v*
and L*a*b* express Y by Y = 0.299R + 0.587G + 0.114B,
while spaces HSI, HSV, and HSL use Y = I = (R+G+B)/3,
Y = L = M ax(R, G, B) and Y = L = (M ax(R, G, B) +
M in(R, G, B))/2 respectively.
(a)
We define luminance L as a linear combination of M ax(R, G, B)
and M in(R, G, B) as follows :
Q.M ax(R, G, B) + (1 − Q).M in(R, G, B)
L= (1)
2
where Q = eαγ is a parameter that allows a tuning of
the variation of luminosity between a saturated hue (color)
(b)
and a hue containing a great amount of white, with α =
M in(R,G,B) 1
.
M ax(R,G,B) Y0
and Y0 = 100. γ is a correction factor
whose value (= 3) coincides with the one used in L*a*b*
space. It should be noted that when M in(R, G, B) = 0 and
M ax(R, G, B) varies between 0 and 255, luminance L takes
a value between 0 (black) and 128. When M ax(R, G, B) =
255 and M in(R, G, B) varies between 0 and 255, luminance
takes a value between 128 and 135.
In a similar way, we define chroma C = Q.Cn where Cn (c)
represents a mixture of three different combinations of R,
G, and B components: red-green, green-blue and blue-red.
The proposed formulae for C (Equation 2) ensures linearity
within lines/planes of hue (see Figure 3-d).
Q. R − G| + |G − B| + |B − R|
C= (2)
3
(d)
The hue value can be computed using the following equation:
Figure 3: a) and c) HCL color space model with H com-
puted using Equations 4 and 5 respectively. b) and d)
G−B Variation of chroma C and luminance L for six different
H = arctan (3)
R−G hue values.
However, hue values (Equation 3) vary between −900 and
+900 only. To allow hue values to vary in a larger interval
Figure 3 shows the HCL color model where Figures 3-a and
going from −1800 to 1800 we propose the following alternate
3-c are obtained using formula L, C as well as H computed
formula (see figures 3-a and 3-c):
using Equations 4 and 5 respectively. We can notice that
the two variants of the HCL model (according to the two
ways the hue H is computed) have a uniform hue angle.
The chroma C decreases while the luminance L increases
if ((R − G) < 0 and (G − B) ≥ 0), then H = 180 + H according to an increase of the white color. In Figure 3-
if ((R − G) < 0 and (G − B) < 0), then H = H − 180 . b, the following colors: red, yellow, green, cyan, blue and
(4) purple have a unique angle whose value is 00 , 900 , 1350 ,

4. 1800 , 2700 and 3150 respectively. In Figure 3-d, the angle G=255, B=0). This reference color appears on the leftmost
is 00 , 600 , 1200 , 1800 , 2400 et 3000 respectively. Such result top cell of each figure. The most similar colors returned by
shows that HCL model offers a better hue constancy than the selected distances (e.g., Euclidean, E94 , Dcyl ) are dis-
L*C*H et CIECAM02 models. played in a decreasing order of similarity from left to right
and top to bottom. Figures 4 to 6 give the sequences of
4. COLOR SIMILARITY MEASURES colors returned by the Euclidean distance applied to RGB,
The notion of uniform color perception is an important cri- L*a*b* and L*C*H* respectively. Figures 7 and 9 show the
terion for classification and discrimination between color list of colors returned by the application of E94 to the
spaces. In order to capture perceptual uniformity in a color L*C*H* and CIECAM02 spaces. Figures 8 and 10 show
representation space, it is crucial to rely on the distance cri- the list of colors returned by the application of E00 to the
terion which states that the distance D(c1 , c2 ) between two L*C*H* and CIECAM02 spaces while Figures 11 and 12 ex-
colors c1 et c2 is correct if and only if the distance value is hibit the colors returned by the cylindric distance applied to
close to the difference perceived by the human eye [9]. HSV and HCL respectively.
Many distances have been proposed based on the existing From these figures, one can see that the application of the
color models. The Euclidean distance (denoted by E) Euclidean distance to L*a*b* and L*C*H* spaces provides
is frequently used in cubic representation spaces such as the worst answers, i.e., most of the returned colors are not
RGB and L*a*b* and occasionally in cylindric spaces like close to the target color. Such a distance is appropriate to
L*C*H* (see Equations 6 to 8). Another Euclidean-like dis- the RGB space, but is far from being uniform like human
tance (Equation 9) was intensionally proposed for L*C*H perception. However, using the E94 and E00 distances
[1]. In Equation 10, a cylindric distance (denoted by Dcyl ) for color spaces like L*C*H* and CIECAM02 and the cylin-
[10] is used for cylindric and conic spaces like HSL, HSV and dric distance for color spaces like HSV and HCL offers good
L*C*H*. Recently, another formulae for computing color results with a slight superiority of the HCL space (see Fig-
difference (denoted by E00 in Equation 11) has been pro- ure 12) we defined in this paper. However, all the provided
posed in [5]. results are not completely compatible with human percep-
tion.
ERGB = R2 + G2 + B2 (6) 4.1 A New Color Similarity Measure
In the following we define a new color similarity measure
called DHCL and based on the cylindric model with param-
Eab = L∗ 2 + a∗2 + b∗ 2 (7) eters AL and ACH . This measure is particularly adapted to
the new color space defined in this paper.
ECH = L∗ 2 + C∗2 + H ∗2 (8)
DHCL = (AL L)2 + AH (C1 2 + C2 2 − 2C1 C2 cos( H)) (12)
L∗ 2 C∗ 2 H∗ 2
E94 = + + (9)
kL SL kC SC kH SH where AL is a constant of linearization for luminance from
√ the conic color model to the cylindric model, and AH is a
where kL = kC = kH = 1, SL = 1, SC
√ = 0.045 C1 C2 +
1 and SH = 0.015 C1 C2 + 1 parameter which helps reduce the distance between colors
having a same hue as the hue in the target (reference) color.
Dcyl = L∗ 2 + C ∗ 1 2 + C ∗ 2 2 − 2C ∗ 1 C ∗ 2 cos( H) (10)
In order to determine these two parameters, we consider a
slice of the HCL model. For example, let us take a refer-
ence pixel Pr of saturated purple (see Figure 3). We can see
that a pixel Pa with the same hue ( H = 0) and the same
L∗ 2 C∗ 2 H∗ 2
E00 = + + + R (11) luminance ( L = 0) with a difference in chroma equal to
kL SL kC SC kH SH C = 50 is more similar to pixel Pr than pixel Pb having
We have conducted an experimental study to first analyze L = 0, C = 0 and H close to 80. Then, we can deter-
the compatibility between these distances and the color spaces mine ACH as ACH = H + 8/50 = H + 0.16. Moreover,
HSV, L*C*H* and CIECAM02, and then contrast these dis- the pixel Pb is more similar to pixel Pr than the pixel Pc hav-
tances against human perception. To that end, we have se- ing H = 0 and C = 50, and being darker ( L = 37).
lected ten different colors as reference (target) colors. Each However, the pixel Pd with H = 0, C = 50 and a greater
one of them is compared to a collection of randomly gener- luminance ( L = 25) is more similar to pixel Pr than pixel
ated colors using each one of the proposed similarity mea- Pb does. Due to this luminance effect, we proceed to a tri-
sures. Colors are generated automatically by a variation of angulation computation which leads to a correction factor
R, G and B values (0 ≤ R, G, B ≤ 255) using an increment equal to AL = 1.4456.
equal to 15. This leads to a set of 4913 colors for each color
space. Figure 13 illustrates the output provided by the new simi-
larity measure DHCL when it is applied to the HCL color
To illustrate the potential of the new color space HCL de- space. One can notice that the returned colors are closer to
fined earlier, Figures 4 through 12 show an experimental the reference color (leftmost top cell) than those obtained
case using a fully saturated and pure yellow color (R=255, using existing color distances and spaces (see Figures 4 to

5. Figure 10: Distance E00 applied to CIECAM02 space.
Figure 4: Euclidean distance applied to RGB space.
Figure 11: Cylindric distance Dcyl applied to HSV
space.
Figure 5: Euclidean distance applied to L*a*b* space.
Figure 12: Cylindric distance Dcyl applied to HCL
Figure 6: Euclidean distance applied to L*C*H* space. space.
11) or using Dcyl with the new HCL color space (see Figure
12). Experimental results on reference colors other than yel-
low confirm that the application of the new color distance
DHCL to the new color space HCL leads to a better per-
ceptual uniformity than HSV, HSL, L*a*b* et L*C*H* for
which existing distances are used (see Equations 6 to 10).
Figure 7: Distance E94 applied to L*C*H* space.
Figure 13: New distance DHCL applied to HCL space.
Figure 8: Distance E00 applied to L*C*H* space.
4.2 Empirical Analysis
In order to compare the sequence of colors returned by the
computer system (according to different color spaces and
distances) with the list returned by the human system, seven
subjects were asked to evaluate the output. For each one of
the ten cases (see Figures 4 to 13) corresponding to pairs of
a given color space and a color distance, there are 48 cells:
the reference color cell (leftmost top cell) and 47 (returned)
Figure 9: Distance E94 applied to CIECAM02 space. color cells. Every subject has to choose and rank the top
ten colors that are most similar to the reference color. If less
than ten colors are selected by a subject for a given combina-
tion of color distance and space (e.g., Euclidean distance and

6. RGB), then the rank of missing colors is given the value 48. HCL outperforms the other combinations of color distances
At the end of the experimentation, all subjects concluded and spaces. The pair E00 and CIECAM02 provides good
that using DHCL on HCL leads to better results than the results for yellow and green but the worst effectiveness ratio
other combinations of distance and space. Indeed, the com- for the three other colors. The pair E94 and L*C*H* gives
bination of DHCL and HCL returns much more colors that the worst retrieval effectiveness for all the selected colors.
are similar to the reference color than any one of the other
combinations. Moreover, we conducted additional empirical studies to com-
pare the proposed color space HCL against L*C*H* and
Figure 14 exhibits five rows corresponding to different colors. CIECAM02 on an image data set of 3500 images repre-
The first cell in each row identifies the reference color (red, senting photographs et paintings of small, medium or high
yellow, green, blue and purple) while the remaining cells resolution. This includes 500 images from the database of
have a rank from 1 to 12 where rank 1 corresponds to the the Info-Muse network [4] containing museum collections in
color which is the most similar to the reference color. The Qu´bec (Canada) as well as images from different web sites
e
ranking is computed as the mean of the judgment of seven [11]. The first set contains art images related to paintings,
subjects, three of them are experts in image processing. statues, medals and ancient clothing items. The whole col-
lection is grouped under four overlapping semantic classes:
painting, close-up, indoor and outdoor images. Each class
(e.g., Outdoor) is further split into subgroups (e.g., city,
landscape, etc.).
Figure 14: Five reference colors with the average rank-
ing of similar colors (from 1 to 12).
Figure 15: Ranking according to eight pairs of distances
and color spaces.
Figure 15 provides the ranking for the purple color. The first
row corresponds to the ranking (from the most similar to
the less similar) using the distance Dcyl and the HCL space
defined in the paper. The remaining rows give the ranking
returned by the pairs Dcyl and HSV, E and L*a*b*, E
and L*C*H*, E94 and L*C*H*, E00 and L*C*H*, E94
and CIECAM02, and E00 with CIECAM02, respectively.
To quantify the potential of each distance to return the col-
ors that are close to human perception, we have applied the
following effectiveness measure (see [6] for more details).
Figure 16: Retrieval effectiveness of six combinations of
distances and color spaces.
Rc
1 i=1 i
Ef fsys = R Rc Rc
. (13) Based on our previous work on similarity analysis [6], the
1 + log( Rc ) i=1 i+ i=1 |i − ri |
comparison between two images makes use of color histograms
where Rc is the total number of relevant colors (according to and a similarity distance involving the Dirichlet distribution.
the user’s judgment) in the color set, R is the total number Figures 17 through 19 illustrate the retrieval output pro-
of retrieved colors (R ≥ Rc ), i (= 1, 2, · · · , Rc ) is similarity vided by the system when CIECAM02, L*C*H* and HCL
image ranking by human judgment and ri corresponds to color spaces are used, respectively. When an image query
system image ranking (in a decreasing relevance order). (leftmost top image) is submitted, the system returns im-
ages in a decreasing order of similarity. A careful look at
The curves in Figure 16 illustrate the retrieval effectiveness the three figures indicates that HCL outperforms the two
ratio of color distance and space combinations pour five ref- other spaces. For example, one can see that the first two
erence colors where the ordinate represents the average ef- rows in Figure 19 contain images with colors closer to those
fectiveness computed from the judgment of seven subjects. in the image query than images in the same rows of Figures
One can see that the combination of DHCL and color space 17 (CIECAM02) and 18 (L*C*H*).

7. 5. CONCLUSION
In order to overcome the limitations of existing color spaces
and color distances in correctly capturing color differences
perceived by the human system, we have presented a new
color space called HCL inspired from HSL/HSV and L*a*b*
spaces as well as a new similarity measure labelled DHCL
and tailored to the HCL space. Experimental results show
that using DHCL on HCL leads to a solution very close to
human perception of colors and hence to a potentially more
effective content-based image/video retrieval.
We are currently studying the potential of our findings in
three fields of image/video processing, namely : image seg-
mentation, object edge extraction, and content-based image
(or sub-image) retrieval.
Acknowledgments
The authors would like to thank the anonymous reviewers
for their valuable comments and suggestions for improve-
Figure 17: Image retrieval using CIECAM02 color ment. This work is part of CoRIMedia research projects that
space. are financially supported by Valorisation Recherche Qu´bec,
e
Canadian Heritage and Canada Foundation for Innovation.
Figure 18: Image retrieval using L*C*H* color space.
Figure 19: Image retrieval using HCL color space.

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