FCV2016, the 22nd Korea-Japan Joint Workshop on Frontiers of Computer Vision

Modeling of “High-Class Feeling”
on a cosmetic package design
Kae Nakajima, Nayoung Lee, Kensuke Tobitani,
Kenji Katahira and Noriko Nagata

Aya Shiraiwa

School of Science and Technology / Research Center for Kansei Value Creation
Kwansei Gakuin University
Hyogo, Japan
Email: nakajima.k@kwansei.ac.jp

Kunio Nikata

Kaoru Arakawa

Liberal Arts
Kanazawa College of Art
Ishikawa, Japan

Department of Frontier Media Science
Meiji University
Tokyo, Japan

Abstract—The platform of personal fabrication is developedby technologies such as 3D printing. However, most people do
not have enough professional knowledge or skills to design theseproducts. One promising approach involves relating subjective
impressions to physical parameters in order to support intuitive
design. This study aimed to build a model that predicts a “highclass feeling,” which is a product value, using physical features.
The present study constructed a model that relates the subjective“high-class feeling” of products to physical features. Using a cosmetic compact as an example, a comprehensive high-class feelingand its 5 sub-factors were rated by 20 participants, and regression
models were built to estimate those ratings based on physicalparameters. This study suggests that a comprehensive high-classfeeling is well explained by “elegance” and “luxuriance” andis better estimated indirectly via these sub-factors than directlyfrom physical features.

I. I NTRODUCTION
In recent years, digital machine tools, such as 3D printing, have spread remarkably and have brought transformativechanges to the manufacturing industries. Nowadays, individuals are able to generate information worldwide through theInternet, which means that personally transmitted music andmovies can become popular worldwide. By making personal
fabrication popular, personally transmitted contents have been
spread to actual manufacturing industries.
However, it is not easy for non-professional ordinary people
to create exactly what they want using personal fabrication
platforms. For example, most people do not have enough professional skills to design and create a container with warmthand elegance. Therefore, to realize the world of personalgenerated manufacturing with full sensibility, it is necessary to
study how to connect Kansei (sensibility) with manufacturing.
To design Kansei into objects, at ﬁrst, it is necessary to know
subjective impressions to physical parameters.
From now on, in studies of the evaluation of productdesign and image quality or studies of personal perception for
texture we have aimed to transform subjective impressions into

Faculty of Engineering
Tottori University
Tottori, Japan

Kampo Ishii, Fumihiko Tsukiyama
and Shinji Kobayashi
KOSÉ Corporation
Tokyo, Japan

physical parameters. Kinoshita et al. [1] has studied the designof mobile phone as an example with the aim of re- storing
human “attitudes” or “images” to morphological elements(physical elements), which can be used as knowledge of design
planning. They suggest a hierarchical model consisting of“attitude,” “image,” and “cognition,” and they focused on theindividual differences in the evaluation of the product design.
According to them, “attitude” is connected directly with thepurchasing of consumers, such as the emotions of “liking” or“wanting to buy,” which is the big difference in individual
evaluations. “Image” is what most people conjure up in theirmind, such as “feminine” or “mannish.” “Cognition” is whatmost people recognize as being the same such as, “Thisis round.” [1]. Aiba et al. [2] showed that physical factorsof noise, luminosity, and contrast inﬂuence the subjective
evaluation of human, high-level sensing, such as applying thewords “beautiful,” “like,” or “good,” to image quality.
On the other hand, Aoyama et al. [3] revealed the relationship between the quality of image features and the feelings
evoked in humans when looking at those images, such as“roughness” or “glossiness.” They also arranged the engineering modeling of the feeling of a material. In their study, theimpression of the texture of the images of familiar materials (fabric/leather/construction ﬁnishing materials), such as“napped/not napped” or “glossy/not glossy,” corresponds toimage features obtained from images directly by a multivariate regression model [3]. In addition, many studies on thequantiﬁcation of texture have been conducted recently, suchas the estimation of metal texture by CG parameter [4].
In those studies on product design, a hierarchical model ofKansei has been suggested. In studies about texture, which
is closed to the characteristics of the material and has lesssubjective difference according to the Kansei quantiﬁcation of
texture, has been conducted by image feature amount. This
study aimed to build a technological model by connecting
physical value (image features amount) to impressions and-319-

subjects related to higher-order Kansei values. “High-classfeeling” is the target, and it is estimated that it is a higher
level of Kansei value than texture.
“High-class feeling” is one of most important Kansei values
when it comes to promoting brand names and products. In
particular, cosmetics are required for a psychological Kansei
value, and “high-class feeling” is the most important keyword
that many cosmetic products adopt as their concept. Amongthe components of a cosmetic product, package design is thedesign of the packaging, which intends to promote “highclass feeling” dramatically [5], [6]. Thus, many packages
have been made with “high-class feeling” in mind. Inaba[6] introduced several examples of products and summarized
the characteristics of “high- class feeling” evident in theirpackages. However, quantiﬁcation of “high-class feeling” hasnot been conducted by physical parameters. Thus, in this study,
using a cosmetic package as an example, the evaluation ofsubjective impressions has been conducted and the amountof physical features has been abstracted, in order to reveal
the relationship between the “high-class feeling” of a productpackage design and the amount of physical features. Then,
this study aimed to build a model of “high-class feeling”
technologically.
II. M ETHODS
A. Selection of Evaluative items for “High-Class Feeling”
We have collected evaluative terms for “high-class feeling”
from prior studies [6], [7], and we adopted 5 sub-factors; “elegance,” “harmony,” “perfection,” “comfort,” and “luxuriance,”
to evaluate the “high-class feeling” of a cosmetic package.
Then, these 5 sub-factors were deﬁned as the factors thatconsist of “high-class feeling.” In addition to these 5 subfactors, another factor—involving a comprehensive question
about “high-class feeling”: “Do you feel “high-class feeling”
in relation to the product?”—was adopted. A total of 6 items
was adopted in the evaluation experiment.
B. Evaluation Experiment of “High-Class Feeling”
20 university students (Male: 10, Female: 10, Average:
22.25 years old, SD=1.02) participated. 32 colored images oflids of cosmetic packages, mainly cases of foundation (Fig 1,2), were used. The experimental stimuli were presented on aliquid crystal display (CG246, EIZO). Current stimuli were
presented at the upper side of the display, and there was asample image presented in the middle of the display. Then, anevaluation scale of 6 items with 7 ratings each was presented at
the lower side of the display. “Return” and “Conﬁrm” buttons
were arranged at the bottom of the display. The backgroundof the display was colored neutral grey (R=G=B=155). The
size of the sample stimuli images was set at almost the same
size as when people held it in their hand, which was width
11.5cm and visual distance 76cm.
Using a mouse operation without a time limit, participants
rated sample stimuli on the display in relation the “high-classfeeling” they gained for each of the 6 items using a 7-point
Likert scale. After ﬁnishing rating all 6 items, participants

clicked the “conﬁrm” button and it moved on to the next
display. If a participant clicked the “return” bottom, they
could rate the prior question again. The sample stimuli were
presented in random order on a display for each participant.
C. Calculation of Image Features
We focused on the color, glossiness, and texture of acosmetic package sample, and calculated the image feature
amount to express these features. To calculate the image
feature amount, the color space of the image was converted
from RGB color space to HSV color space, and the Histogram amount for Hue (H), Saturation (S), and Value (V)was obtained. HSV color space is similar to human color
perception[8]. Color detection in the image recognition processing is applied based on the HSV color space; therefore,
we adopted HSV color space in this study.
To obtain the amount of features used to describe colors, we
extracted the median of the histogram for each H, S, and V. We
also extracted the variance of the H histogram. Furthermore,in terms of the amount of features to describe glossiness,the skewness of the V histogram was extracted. As for thefeature amount needed to describe texture, the entropy andstraight-line ratio (the proportion of straight edges/total edges)of the V histogram were also extracted. Skewness describedthe distortion of the histogram distribution, and it is recognized
as an image feature amount relating to glossiness [3]. It is
assumed that entropy shows the amount of information [9]. To
apply to digital fabrication to the Internet, where low quality
images exist, these image feature amounts were comparatively
simple, and above 7 features were adopted according to thepreliminary experiment.
III. R ESULTS
A. Analysis of Impression of “High-Class Feeling”
In order to classify the impression of “high-class feeling”
in relation to cosmetic packages, we applied hierarchicalcluster analysis (Ward’s method) to get the rating value of
5 sub-factors of “high-class feeling” to 32 stimuli, against the
averaged data of participants for each sub-factor (32 samples
x 5 sub- factor). From the obtained dendrogram (Fig.1), weclassiﬁed the information into 4 clusters. The evaluated proﬁleof each cluster is indicated in Fig.2. To compare the comprehensive “high-class feeling” as it occurs between 4 clusters,
we conducted analysis of variance (ANOVA) with cluster as
the main factor for the evaluation value of a comprehensive“high-class feeling.” As the result, there was signiﬁcant main
effect of cluster [F (3, 28) = 19.72; p < .001]. Then, multiple
comparison (Tukey’s HSD method) was applied, showing thatcluster 2 and 4 had signiﬁcantly higher levels of comprehensive “high-class feeling” than cluster 1,3 (between 1,4:
p < .005; all of others: p < .001). Between cluster 2 and 4 andcluster 1 and 3, there was no signiﬁcant difference (p > .5).
From these results, cluster 2,4 was interpreted as a high-“highclass feeling” cluster and cluster 1,3 was interpreted as a low“high-class feeling” cluster.

-320-

A

Cluster 1

B

Cluster 2

Cluster 3

Cluster 4

6

****
****

5
4

rt
fo
m
Co

Lu

n
io

on
rm

Ha

El

eg

an

ce

2

y

3

ct

12

rfe

10

xu
C
ria
hi om
nc
gh pr
e
-c eh
la en
ss s
fe iveelingCluster 4

8

****
***

Cluster 3

6

Evaluation value

Cluster 1

4

29
13
30
12
16
10
17
7
15
6
31
27
28
9
8
4
24
26
20
25
2
22
21
11
5
32
3
23
14
1
19
18

Cluster 2

2

Pe

0

Sub-factors

Fig. 2. Evaluation proﬁle of 4 ”high-class feeling” clusters

than other sub-factors. (Between “harmony” and “elegance”
on cluster 2: p < .05; all others p < .005).

Fig. 1. Clusters of ”high-class feeling”

B. Cluster of “High-Class Feeling” and Image Features
To reveal the characteristics of each cluster, two-way (4clusters x 5 sub-factors) mixed ANOVA was performed for
rating the value of the samples. Greenhouse-Geisser corrections were performed when Mauchly’s sphericity test wassigniﬁcant. As for the result of the ANOVA, the main effects ofclusters and sub-factors in terms of “high-class feeling” weresigniﬁcant [F (3, 28) = 61.56, p < .001; F (1.40, 39.32) =11.80, p < .001]. Interaction between cluster and sub-factors
in “high-class feeling” were also signiﬁcant [F (4.21, 39.32) =13.82, p < .001]. The simple main effects of each subfactor for “high-class feeling” were signiﬁcant for all of thecategories (all, p < .001). For “perfection” and “luxuriance,”cluster 2,4 was signiﬁcantly higher than cluster 1,3 (“luxuriance” between 2,3: p < .05; all others p < .01). Both cluster
between 2,4 and between 1,3 had no signiﬁcant difference(p > .05). Between cluster 2 and 4, there were signiﬁcantdifferences for “elegance,” “harmony,” and “comfort,” andthe rating value for cluster 2 was higher than for cluster 4
in all categories (all, p < .001). The simple main effectsof sub-factors for “high-class feeling” for each cluster weresigniﬁcant (p < .001, onlycluster3 : p < .01). “Elegance” incluster 2 and “luxuriance” in cluster 4 were the items with thehighest signiﬁcance value, and these were recognized more

To reveal the relationship between the clusters for the “highclass feeling” of cosmetic packages and physical features, oneway ANOVAs were performed with cluster as a factor foreach image feature (Fig. 3). A homoscedasticity of medians ofsaturation (S) and value (V) was not satisﬁed. We applied nonparametric Kruskal-Wallis, which didn’t have an assumptionof equal variances. As a result, for the median of saturation[χ2 (3, N = 32) = 8.733; p < .05] and the median of value[χ2 (3, N = 32) = 12.816; p < .01], the main effect of cluster
was signiﬁcant. Applying multiple comparison (Steel-Dwass
test), there were signiﬁcant differences in that the median of
the saturation of cluster 3 was higher than cluster 2,1, and the
median of value of cluster 4,3 was higher (both, p < .05).
We did not ﬁnd any other signiﬁcant main effect of cluster forother image features.
C. Modeling of “high-class feeling”
To establish a “high-class feeling” estimation model fromthe image features, we assumed that a comprehensive “highclass feeling” was expressed by 5 sub-factors of “high-classfeeling.” Based on this assumption, we established a 3 layered
model to estimate sub-factors from image features and acomprehensive “high-class feeling” from sub-factors. At ﬁrst,

-321-

120

0.020

model. The 2-layer model was calculated from the image
features directly, without concerning the factors of “high-classfeeling.” Speciﬁcally, stepwise, a multiple regression analysis
was applied for the rating value of a comprehensive “highclass feeling” as an objective variable, and 7 image features
were used as an explanatory variable (Tab. III). The 2-layered
model was applied only for “V. median” as an explanatory
variable; therefore, it became an invalid model (Adjusted
R2 = .155). We estimated the rating value of “high-classfeeling” from 7 image features using both the 2-layered modeland the 3-layered model. A correlation coefﬁcient between
measured value was r = .427 for the 2-layered model andr = .533 for the 3-layered model (Fig. 4). As mentioned
above, better predictability was obtained by the 3-layered
model, which we suggested instead of the 2-layered model.

H. variance

H. median

100
80
60
40

0.015

0.010

0.005
20
0

1

2

3

0.000

4

1

2

300

160

250

120
80

4

3

4

200
150
100

40

50

0

0
1

2

3

4

1

Cluster
14

V. entropy

12

V. skewness

3

Cluster200

V. median

S. medianCluster

10
8
6
4
2
0
1

2

3

4

Cluster2

Cluster

10
9
8
7
6
5
4
3
2
1
0

TABLE IM ULTIVARIATE REGRESSION MODEL OF 5 SUB - FACTORS BY PHYSICALFEATURES

Explanatory Standardized
Adjusted R2
variable
coefﬁcients β
V. median
0.514 **** 0.405 ****
S. median-0.311 *luxuriance V. entropy
0.471 ***
0.411 ****
S. median-0.336 *V. skew
0.309 *
harmony
V. median
0.594 **** 0.519 ****
V. entropy-0.313 *S. median-0.298 *perfection
V. skew
0.530 **** 0.371 ****
V. median
0.506 ***
comfortV. median
0.537 **** 0.504 ****
V. entropy-0.361 **S. median-0.330 ***** p < .001, *** p < .005, ** p < .01, * p < .05
Objective
variableelegance

1

2

3

4

Cluster

Straight-line ratio

0.50
0.40
0.30
0.20
0.10
0.00

1

2

3

4

ClusterFig. 3. Image features of 4 ”high-class feeling” clusters

a stepwise multiple regression analysis was performed, with 7
image features as an explanatory variable, and 5 sub-factorsof “high-class feeling” as an objective variable. As a result,
we obtained the modeling of the explanatory variable; “V.
median” and “S. median” for “elegance,” and “entropy,” “H.
median,” and “V. skewness” for “luxuriance” (Tab. I). In
the same way, we obtained the modeling of an explanatory
variable: “V. median,” “V. entropy,” and “S. median” for“harmony” and “comfort,” and “V. skewness” and “V. median”
for “perfection” (Tab. I). Next, a stepwise multiple regression
analysis was performed, with 5 sub-factors of “high-classfeeling”; “elegance,” “harmony,” “perfection,” “comfort,” and“luxuriance” as explanatory variables, and the rating value ofa comprehensive “high-class feeling” as an objective variable.
As a result, we obtained persuasive modeling of an explanatory
variable of “elegance” and “luxuriance” (Adjusted R2 = .919,
Tab. II).
We established a 3-layered model of “high-class feeling”
by plugging in the model estimated values of “elegance” and“luxuriance” (Tab. I) for the comprehensive “high-class feeling” model (Tab. II). The estimation accuracy for the “highclass feeling” of this model was compared to the 2-layered

TABLE IIM ULTIVARIATE REGRESSION MODEL OF HIGH - CLASS FEELING BY 5
SUB - FACTORS
Explanatory Standardized
Adjusted R2
variable
coefﬁcients β
luxuriance0.743 **** 0.919 ****
elegance
0.534 ******** p < .001, *** p < .005, ** p < .01, * p < .05
Objective
variablehigh-class feeling

TABLE IIIM ULTIVARIATE REGRESSION MODEL OF HIGH - CLASS FEELING BYPHYSICAL FEATURES

Objective
Explanatory Standardized
Adjusted R2
variablevariable
coefﬁcients β
high-class feeling V. median
0.427 *
0.155 ***** p < .001, *** p < .005, ** p < .01, * p < .05

IV. D ISCUSSION
Two high-“high-class feeling” clusters were obtained byanalyzing the impressions of “high-class feeling.” The evaluation values of “luxuriance” and “perfection” were higher-322-

Estimate value

A

B

7
6
5
4
3
2
1

r=.427
1

2

3

4

5

6

7

7
6
5
4
3
2
1

r=.533
1

2

3

4

5

6

7

Measured value
Fig. 4. Correlation between model estimated values and measured value

2-layered model (A), 3-layered model (B).

for high-“high-class feeling” clusters than for low-“high-classfeeling” clusters. This result indicated that a sample withhigh- “luxuriance” and high-“perfection” was rated as a highcomprehensive “high-class feeling.” Each of the values of“elegant,” “harmony,” and “comfort” had signiﬁcant difference
between cluster 2 and 4, which belonged to the high-“highclass feeling” cluster. It can be seen that “high-class feeling”
had several patterns [6]; thus, these 3 items would express
different pattern of “high-class feeling” when “luxuriance” and“perfection” are sufﬁciently high.
In the comparison of image features in the “high-classfeeling” impression cluster, there was a difference only in theclusters “S. median” and “V. median” for 7 image features.
“S. median” was signiﬁcantly lower in cluster 1,2, compared
to cluster 3. Common impression features of cluster 1,2 werehighly rated for “elegance,” “harmony,” and “comfort.” On theother hand, “V. median” was signiﬁcantly lower for cluster 3,4,
compared to cluster 2. One common feature of cluster 3,4 wasthat the rating values of “elegant,” “harmony,” and “comfort”
were lower than the other clusters. From these results, it can beassumed that “elegance,” “harmony,” and “comfort” are rated
highly against the package design with low saturation and highvalue. In the multiple regression analysis for 5 sub-factors byimage features, “S. median” and “V. median” were adopted
as an explanatory variable for each model of “elegance,”“harmony,” and “comfort,” suggesting that those color features
have a relationship with those “high-class feeling” factors.
“Entropy” was also adopted as an explanatory variable of“harmony” and “comfort,” and its coefﬁcient was negative.
Because “entropy” indicated texture information amount, it
can be interpreted that a simple package design with lesstexture creates impressions such as “harmony” and “comfort.”
For this reason, it can be suggested that a luminous-coloreddesign gives an impression of “elegance” and that luminouscolored and simple designs give an impression of “harmony”
and “comfort.”
In the comparison of the image features between the impression clusters for “high-class feeling,” there was no signiﬁcantdifference between the clusters except “S. median” and “V.

median,” which were image features about color. However,many glossy samples belonged to high-“high-class feeling”
cluster 2,4, while the value of “skewness,” which was an image
feature related to glossiness, was greater than the low-“highclass feeling” cluster 3,4. The cluster 4 had many decorative
samples and was marked by the impression of “entropy,”
which indicated that the amount of information was big incluster 4. According to the results of a multiple regression
analysis for 5 sub-factors by image features, both “skewness”
and “entropy” were adopted as an explanatory variable for“luxuriance,” while “skewness” was adopted for “perfection.”
Based on these results, it can be suggested that the information
amount of glossiness is possibly related to “perfection,” while
the information amount for glossiness and texture may berelated to “luxuriance.”
In the modeling of “high-class feeling,” a 3-layered “highclass feeling” modeling, with an impression of the “highclass feeling” factor, had better estimation accuracy relatively
than a 2-layered model, which estimated “high-class feeling”
directly from image features. In the 2-layered model, theonly signiﬁcant explanatory variable was “V. median,” which
expressed color. On the other hand, the 3-layered model
could express “high-class feeling” with 5 image features: “V.
median,” “S. median,” and “H. median” as the color features
amount, “skewness” as the glossiness feature amount, and“entropy” as the texture feature amount by intermediating“elegance” and “luxuriance.”
As mentioned previously, in an analysis of the “highclass feeling” impression, it was presumed that a feature of“luxuriance” and “perfection” was required to obtain high“high-class feeling”; however, the modeling of “high-classfeeling” adopted “elegance” and “luxuriance” as “high-classfeeling” factors. It was assumed that the required impressions
for “high-class feeling” were concentrated in “luxuriance” andthat the characteristic impression of “high-class feeling” was“elegance.” This was presumed because the image features thatwere adopted for the modeling of “luxuriance” and “elegance”
covered all features adopted for the modeling of “harmony,”“perfection,” and “comfort.”
V. C ONCLUSION
In this study, which was fundamentally a discussion of manufacturing with personal Kansei by means of digital fabrication, we investigated the relationship between “high-class feeling” and physical features using an impression evaluation experiment. As a result, it was suggested that high “luxuriance”
and “perfection” were essential impressions of “high-classfeeling” in relation to cosmetic packages and that “elegance,”“harmony,” and “comfort” were distinguished impressions fora pattern of “high-class feeling.” It was suggested that therelationship between “high-class feeling” impression and theimage features of “luxuriance” and “perfection” mainly related
to the height of skewness (glossiness) and “elegance,” while“harmony” and “comfort” related to the median saturation andvalue. Furthermore, in terms of the presumption of a “highclass feeling” from image features, it was suggested that the-323-

related to the median saturation and value. Furthermore, interms of the presumption of a “high-class feeling” from image
features, it was suggested that the 3-layered model with factorsof “high-class feeling” could presume “high-class feeling”
more precisely than the 2-layered model without factors of“high-class feeling.” This result showed the suitability ofthe method of expressing Kansei using a hierarchical model,
which Kinoshita et al. [1] have suggested.
This study suggested the suitability of a Kansei hierarchizedmodel; however, the estimation accuracy obtained by the 3layered model was not good enough. To make more accurate
modeling, it will be necessary that numbers of samples ofstimuli should be increased. Also, it is necessary to review thefactors of “high-class feeling” and image features. Moreover
we need to build the model besides the sample of cosmetic
packages, and then we need to discuss the versatility of themodeling.
ACKNOWLEDGMENT
This research is partially supported by the Center of Innovation Program from Japan Science and Technology Agency,
JST.
R EFERENCES[1] Kinoshita Y., Inoue K., Sakai M.: Investigation analysis of mobile phone
design from difference between man and woman, Japan Society of Kansei
Engineering, 7, 3, pp. 449-460, 2008.
[2] Aiba E., Numata K., Fujisawa T., Nagata N.: Subjective image-quality
assessment based on psychological measurement : Identiﬁcation of Physical Factors Related to Assessment, The Institute of Image Electronics
Engineers of Japan, 40, 5, pp. 792-800, 2011.
[3] Aoyama Y., Kato T., Sudo K., Taniguchi Y.: Modeling of sense of materialof object surface - Proposal of image features based on binocular parallax-, IPSJ SIG Technical Report, 2014, 8, pp. 1-6, 2014.
[4] Yata N., Furukawa T., Manabe Y.: Determinants of metallic perception
based on subjective evaluation, Journal of the Color Science Associationof Japan, 37, 2, pp. 124-131, 2013.
[5] Senoo M.: Feeling of luxury as a Kansei-quality, The Society of Cosmetic
Chemists of Japan, 45, 4, pp. 291-296, 2011.
[6] Inaba T.: Color expression which makes ”high-grade” imagine in acosmetics package, Cosmetic stage, 7, 7, pp. 1-10, 2013.
[7] Tsurusaki K., Yamaoka T.: Systematizing design means to obtain intended
images in consumer electronic products, Japan Society of Kansei Engineering, 6, 3, pp. 47-52, 2006.
[8] Takahishi K., Matsuura M., Sugiyama T., Abe K.: Comparison of color
coordinate systems based on human-made color classiﬁcation and image
segmentation, IEICE trans. D-II, 84, 7, pp. 1378-1388, 2001.
[9] Motoyoshi I., Nishida S., Sharan V., Adelson E. H.: Image statistics andthe perception of surface qualities, Nature, 447, 7141, pp. 206-209, 2007.

-324-