IW-FCV 2023, Yeosu, Korea

Impression Estimation of Suit Patterns Based on
Style Features Using Multi-scale CNN
12[0000−0002−3898−8435]
Eiki Tsumura , Kesnke Tobitani,
1[0000−0002−2385−4389]
1[0000−0002−2037−1947]
Miyuki Toga
, and Noriko Nagata
1

2

Kwansei Gakuin University, 21 Gakuen, Sanda-shi, Hyogo 6691337, Japan{E.T-tsumu17,toga.m,nagata}@kwansei.ac.jp

University of Nagasaki, 111 Manabino, Nagayo-cho, Nishi-Sonogi-gun, Nagasaki
8512195 ,Japan

tobitani@sun.ac.jp

Abstract. In the eld of fashion design, impressions evoked by the tex-

ture of materials, such as ashy and cool (aective texture), attract
attention. The aective texture is considered necessary in evaluating andjudging the quality of an object and personal preferences. In the fashion
domain, there is a high demand for personalization of designs when diversifying user needs. One example is a custom-made suit service. However,there is a problem that it is labor-intensive to nd a suit that matches
image from many patterns, and colors. It is, therefore, necessary to understand aective texture. Many studies evaluate aesthetics using productstyle, suggesting that style is highly associated with aective texture.
Multi-scale CNN has attracted attention for image recognition and ismore accurate than single-scale CNN. However, no model that combinesstyle and multi-scale CNN has been developed in previous studies.
In this study, we propose a method for aective texture (visual impressions) evoked by suit patterns, corresponding to dierent scales of thepattern. (1) Suit patterns are collected in dierent resolution images,
and impression evaluation experiments quantify (2) aective texture. (3)Model the relationship between the aective texture and physical characteristics (style features) of the pattern images using a multi-scale CNN.
Then, the correlation coecient between the impression values of thetest data and the estimated impression values of the models. The results
showed that multi-scale CNN has better accuracy than single-scale CNN,
conrming the eectiveness of this method.
Keywords: Fashion · Suit · Style · Multi-scale CNN · Impression Esti-

mation Models.

1 Introduction
In the eld of fashion design, impressions (including aesthetics) evoked by the
texture of materials, such as ashy and cool (aective texture), attract attention. The aective texture is considered necessary in evaluating and judging
the quality of an object and personal preferences. In the fashion domain, the- 554 -

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E. Tsumura et al.

impression that clothes make on people plays a vital role in expressing a person's impression [1]. The pattern and color give dierent impressions to people.
Therefore, one must dress appropriately for dierent occasions when wearing asuit. Therefore, technologies are required to quantify, index, and model aective
texture [2].
With the improvement of information technology, users can quickly obtainvarious of product information through the Internet and SNS. User needs andpreferences have become increasingly diverse in recent years, and demand forcustomization and personalization of products and designs has grown. Therefore,
it is essential to accurately grasp users' aective values, such as their preferences
and satisfaction [3].
The tailor-made suit service is an example of the customization and personalization of design in the fashion eld. However, nding a suit that matches
one's taste and image from many available materials, patterns, and colors istime-consuming and labor-intensive. The perception of a pattern's motif may
signicantly aect the pattern's overall impression (a unit that constitutes the
subject of the patterns) [4]. The entire motif is observed at a low resolution if
the motif is large. If the motif is small, the details are observed at high resolution (motifs can be combined to form a concept motif at a higher level). In thisway, people form an impression of the pattern while appropriately changing theresolution according to the size of the motif.
In this study, we propose a method to construct impression estimation models
for suit patterns based on aective texture by modeling the pattern's visual impression and physical characteristics. In particular, we aim to extend the model
to impression estimation using multi-resolution (multi-scale) images to accommodate dierences in pattern scale.

2 Previous Research
Using machine learning techniques, numerous studies have been conducted tomodel the impressions evoked by products and their physical characteristics.
These include technologies that use a product's color, texture, and shape, as
physical characteristics [5] and retrieval technologies based on impressions ofimages [6].
There has been much research on textures [79]. On the deep learning framework, Gatys et al. proposed a style transformation algorithm that content features and style features extracted from VGG19 [10] which is a CNN used forgeneral object recognition [11]. Content features are feature maps output frommiddle layer of VGG19 style features is grammaticalized versions of content
features. Gatys et al. have proposed that content features hold a lot of shapeinformation necessary for general object recognition. In contrast, style features
are texture features that hold many detailed appearance information, such ascolor and pattern, in an image.
Many studies have related to image style and kansei (sensibility) [12, 13].
These studies have built models that use style to evaluate the aesthetics of- 555 -

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Impression Estimation of Suit Patterns

3

photographs [12] and to estimate the aective texture of clothing patterns [13].
The results suggest that style information in images is a feature with a highanity to aective texture. However, since the emotions and impressions evokedby products and textures vary depending on the object's size, an estimation
model that can respond to dierences in the object's scale is desirable.
Multi-scale CNN, a deep learning model that considers dierences in image
scale, has attracted attention in image recognition. Wetteland et al. proposed amulti-scale CNN with pre-trained VGG16 concatenated in parallel [14]. It is aclassication network that takes individual input images of dierent resolutions,and concatenates the features of each resolution image. Global Average Pooling(GAP) and Dropout layer are added to prevent over-learning. The results showedthat multi-scale CNN improved classication accuracy over single-scale CNN.
In this study, we construct impression estimation style features with highanity to aective texture and a multi-scale CNN framework to deal with differences in the scale of suit patterns (motifs). No model that combines style andmulti-scale has been developed in previous studies.

3 Proposed Method
In this study, we propose a method for estimating aective texture (visual impressions) that the suit patterns evoke in response to dierences in pattern scale.
Fig. 1 presents an overview of the proposed method. (1) First, a scanner captured
the suit's fabric which is collected as pattern images at various resolutions. (2)Next, we conducted an impression evaluation experiment on the pattern imageof the suit to quantify the aective texture. (3) We then used the style featureas the suit patterns

physical characteristics. We modeled relationship between

the quantied aective texture and the physical characteristics using a multiscale CNN. Finally, we conducted impression estimation on the test data basedon the constructed models to conrm the proposed method's eectiveness.

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１.

２. Quantification ofCollection of
Suit Pattern Images

Affective Texture

Multi Resolution
Cloth

３.

Construction of
Impression Estimation Models

Vibrant

Estimate

SimpleFig. 1. Overview of our proposed method.

4 Quantication of Aective Texture
4.1 Collection of image data
The suit fabrics were photographed with high dynamic range (HDR) images atdierent resolutions to collect images representing dierent scales. We collectsample images at dierent resolutions for 613 dierent suit cloths based on theimage pyramid of Tada et al. [15]. The resolution was set to 72 dpi, 112 dpi, 224
dpi, and 448 dpi, and a total of 2,452 pattern images were collected. Without lossof generality, capturing with HDR images preserves tone and light regions, resulting in a more transparent representation of woven patterns such as stripes andchecks. However, HDR images cannot be viewed on a standard display, so theymust be converted to low dynamic range (LDR) images, which can be viewed
by compressing their dynamic range. In this study, we used Photographic Tone
Reproduction by Reinhard et al. [16], a representative tone mapping method,- 557 -

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Impression Estimation of Suit Patterns

5and converted the images to LDR images. We normalized all image sizes to 224

× 224pixel. Examples of suit patterns collected from the process are shown in
Fig. 2. A scanner (Epson GT -X830) and software SilverFast 8 were used toperform the analysis. The suit fabrics used for the photography dealt with somefrom the 2016-2019 SS and AW.

72dpi

112dpi224dpi448dpi

StripePattern

Woven
Pattern

CheckedPattern

Pin-Dot
Pattern

Plain

Fig. 2. Pattern images of suits with dierent resolutions.

4.2 Impression evaluation experiment
To quantify the aective texture evoked by suit patterns, we conducted an impression evaluation experiment using crowd-sourcing on a total of 2,452 patternimages of 613 types and four resolutions. We used Lancers as a crowd-sourcing
service. In kansei (sensibility including aesthetics) research, models of kansei areoften expressed in a hierarchical structure [17] consisting of three layers: emotion, impression, and physical element（Fig. 3）. By quantifying the impression
layer, we assume that we can clarify the basis (causal relationship) for the formation of emotional values in the correspondence between people (emotions)and objects (physical characteristics). In the experiment, to select evaluation
words to be used in the experiment, more than 60 words were listed throughdiscussions with the suit experts, and we asked the suit experts to evaluate theirimpressions based on these evaluation words. We conducted factor analysis on
the evaluation data obtained, and we adopted words with high factor loadings as- 558 -

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suitable words for evaluating suit patterns. Table 1 lists the selected evaluation
terms. We selected 44 words, 4 for the emotional layer and 40 for the impression
layer, corresponding to the hierarchical structure of kansei (Fig. 3). The experiment participants were 3,080 of gender and age and were asked to rate a single
stimulus for 20 people.

Emotion
Impression
Physical element

Fig. 3. Hierarchical structure of kansei (sensibility).

Table 1. Forty-four evaluation words were used in the impression evaluation experi-

ment.
crisp

calm

clear

formalplain

uniquevibrant

suitable for everyone

sharp

fashionablesimplesober

well-tailoredadult

standard

youthful

refreshingly cool

dressymassivetrendyelegant

powerfulmodernsexyclassical

kind

neat

smart

suppleprominentsqueezed

stylishgorgeous

good impression

clean

seriouslight feelingcoolhumility

deactivation - activation displeasure - pleasure dislike-likewarm
bad-good

The experimental procedure was conducted using a Likert scale and theAect-Grid method of Russell et al. [17]. Participants were asked to observe
the pattern images of the suits displayed on the screen. They were asked torespond to the degree to which each evaluation term applied to them using aseven-point scale consisting of Not at all Apply, Not Apply, Not Somewhat
Apply, Neither, Somewhat Apply, Apply, and Very Apply. The scores foreach rating scale were divided into 1-point increments, with 1 point representing
Not at all Apply and 7 points representing Very Apply. Two words, displeasure - pleasure and deactivation - activation was evaluated using the AectGrid method. In this study, we used an aect grid of two dimensions, with thehorizontal axis indicating displeasure - pleasure and the vertical axis indicating deactivation - activation. We asked respondents to respond on a nine-point
scale from 1 to 9 for each dimension. The experimental screen is shown in Fig. 4.

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The above process was used to obtain the evaluation data of 44 evaluation words
for 20 participants for each stimulus.
After that, the obtained evaluation data was cleaned for respondents who
were dishonest or only completed the survey for a short amount of time. Whenassessing the stimuli, dummy images are prepared, and those who have not performed the evaluations instructed beforehand on dummy images are considereddishonest respondents. Among the dishonest respondents, the peak (mode) response time was calculated, and those whose response time was shorter thanthe peak were dened as the short time of the respondents. Due to the cleaning
process, 90% of the 3,080 total respondents were valid.
Finally, a discrete probability distribution with the scored evaluation values
as the random variable was used as the impression distribution of the suit patternimages. In this case, the impression distribution was normalized by the numberof raters for each stimulus because the number diered between stimuli based oncleaning the evaluation data. The obtained impression distribution is converted
to expected values and used as the teacher data for the impression estimation
models to be constructed later.

Apply

Sober

(a) 7-point Likert scale.

StressTensionActivation

Displeasure

Melancholy
BoringPleasant
Delight

Pleasure

Deactivation(b) Aect-Grid.

Fig. 4. Experiment screen.

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Relax
Peace

Very Apply

Somewhat Apply

Neither

Not Apply

Not Somewhat Apply

Not at all Apply

Clean

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5 Modeling the Relationships Between Aective Texture
and Physical Characteristics
5.1 Construction of Multi-scale CNN
In this study, we construct a multi-scale CNN based on the deep-learning modelof Wetteland et al. [13] . As a detail of the model to be constructed, the input
images are images of four resolutions: 72dpi, 112dpi, 224dpi, and 448dpi. We use
the pre-trained VGG16 as a feature extractor. VGG16 is characterized by its
emphasis on texture information [19, 20]. We use it as the basis for this model.
Multi-scale CNN places four VGG16s in parallel to generate multiple feature
vectors. These feature vectors are concatenated before being input to the FC
layer. The FC layer has the same size as the original VGG16: 4,096 neurons.
Style features in Gatys et al.'s style transfer [11] are used as physical characteristics to represent the pattern image of the suit. In this case, style features
are a Gram matrix of feature maps from the middle layer of CNN and have been
extracted from the second pool layer of VGG16. However, the dataset is small
and multi-scale CNN has many parameters, making them prone to overtting
during training. Therefore, a Dropout layer was added after each FC layer toprevent overtting. Both Dropout rates were set to 0.5. Since VGG16 is a modelthat solves a classication problem, ia regression problem replaces it when estimating visual impressions. Therefore, we focused on the activation functions ineach layer of VGG16, replacing the ReLU function in the middle layer with thehyperbolic tangent function, and the SoftMax function in the output layer with
the identity function. The VGG16 architecture handled by multi-scale CNN is

Dense
Dropout
Dense
Dropout

Conv 5-1
Conv 5-2
Conv 5-3
Pooling

Conv 4-1
Conv 4-2
Conv 4-3
Pooling

Conv 3-1
Conv 3-2
Conv 3-3
Pooling

Conv 2-1
Conv 2-2
Pooling

Conv 1-1
Conv 1-2
Pooling

input

shown in Fig. 5. The architecture of all four VGG16s is the same as in Fig. 5.

Fig. 5. VGG16 architecture.

5.2 Training
To estimate aective texture of suit patterns, we used the framework describedin Section5.1. In doing so, we solved a regression problem in which the expectedvalue of the impression distribution (impression value) created in Section 4.2
was the objective variable, and the style features extracted from the suit patternimages were the explanatory variables. Multi-scale learning and evaluation were
performed using impression values of 72 dpi, 112 dpi, 224 dpi, and 448 dpi images
as ground truth for a dataset of 2,452 images. Cross-validation was used to train- 561 -

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Impression Estimation of Suit Patterns

9and evaluate the models, and 10-fold cross-validation was used to divide thedataset into Train: Validation: Test = 9: 1: 1 .
The Adam optimizer was used during training. We used Mean Squared Error(MSE) for the loss and evaluation functions. The learning rate was set to 0.001,
the batch size to 64, and the number of epochs to 100. To avoid overtting,
we used Early Stopping, which terminates training when no accuracy improvement is expected. Learning is stopped when there has been no improvement inaccuracy over the past ten epochs.

6 Results and Discussions
6.1 Accuracy comparison between Single-scale CNN andMulti-scale CNN
To validate the eectiveness of the proposed method, we compared the estimation accuracy of the model by 10-fold cross-validation between single-scale CNN
and multi-scale CNN. We use the correlation coecient between the impression
value for the test data and the impression value estimated by the models as ameasure of estimation accuracy. Table 2 shows the correlation coecients forthe impression values of 72 dpi, 112 dpi, 224 dpi, and 448 dpi images as groundtruth for each evaluation term. Table 2 shows the correlation results for the
single-scale CNN in the left column and the multi-scale CNN in the right column. As a result, moderate or higher positive correlations were found for mostevaluation terms. This conrms that the suit pattern's style features have a highanity with people's aective texture. There were 37 evaluation terms an exceptionally high correlation (0.4 or higher), with an average coecient of 0.57.
Among them, deactivation - activation, vibrant, calm, and sober showed
high correlations at all resolutions. We believe that these evaluation words tend
to have high correlations because they are low-order impressions associated withthe color and characteristics of the pattern. The average correlation coecientsfor all evaluation words were more accurate with the multi-scale CNN than withthe single-scale CNN, conrming the eectiveness of the multi-scale CNN.

6.2 Human Visual angle and Resolution
We discuss visual processing and resolution when humans look at the patternof a suit. The visual angle is the angle the object projected onto the eye makes
and is the angle at which the object is viewed. In this study we compare therelationship between the range of resolution at which the human looks the suit
patterns and the resolution in the constructed impression estimation models.
The viewing angle is determined by the size of the visual object and the viewing
distance when observing it and is calculated from Eq. (1).

v = 360/π × arctan(s/2d)(1)

Where v is the viewing angle, s is the object's size, and d is the viewing distance.
We then calculated the resolution at which a person sees the suit pattern from- 562 -

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E. Tsumura et al.

Table 2. Correlation coecients for each evaluation term.

(Left column: single-scale CNN, Right column: multi-scale CNN.)
evaluation word
deactivation activation
displeasure –pleasure
dislike-likebad-good
crisp
clear
plain
vibrant
sharp
simplewell-tailored
standard
refreshingly coolwarm
Massiveelegant
modernclassical
neat
supplesqueezedgorgeous
clean
light feelingcalmformaluniquesuitable foreveryonefashionable
sober
adult
youthful
dressytrendypowerful
sexy
kind
smart
prominent
stylishgood impression
seriouscoolhumility
average72dpi
single-scale multi-scale

112dpisingle-scale
multi-scale

224dpisingle-scale multi-scale

448dpisingle-scale multi-scale

0.79

0.76

0.71

0.69

0.63

0.77

0.70

0.68

0.30

0.40

0.19

0.25

0.39

0.37

0.30

0.43

0.59
0.72
0.64
0.62
0.43
0.76
0.54
0.68
0.16
0.65
0.51
0.42
0.73
0.36
0.16
0.72
0.78
0.16
0.57
0.62
0.40
0.67
0.78
0.75
0.67

0.63
0.68
0.57
0.70
0.68
0.80
0.52
0.74
0.45
0.78
0.63
0.47
0.70
0.39
0.23
0.68
0.75
0.32
0.68
0.72
0.55
0.69
0.82
0.80
0.75

0.42
0.38
0.35
0.30
0.62
0.66
0.29
0.66
0.07
0.60
0.49
0.27
0.64
0.34
0.22
0.44
0.67
0.13
0.51
0.44
0.37
0.60
0.67
0.70
0.68

0.44
0.24
0.46
0.49
0.64
0.74
0.53
0.73
0.20
0.68
0.63
0.35
0.67
0.39
0.30
0.58
0.70
-0.06
0.54
0.50
0.45
0.64
0.76
0.74
0.70

0.34
0.43
0.36
0.57
0.49
0.68
0.25
0.55
0.18
0.63
0.36
0.38
0.63
0.20
0.27
0.29
0.63
0.38
0.44
0.25
0.34
0.35
0.71
0.63
0.59

0.41
0.47
0.47
0.61
0.59
0.71
0.41
0.67
0.07
0.66
0.53
0.47
0.64
0.21
0.23
0.49
0.69
0.29
0.57
0.46
0.40
0.53
0.76
0.68
0.67

0.41
0.35
0.18
0.31
0.46
0.77
0.15
0.64
0.26
0.52
0.48
0.38
0.22
0.23
0.29
0.29
0.58
0.39
0.35
0.55
0.30
0.40
0.71
0.53
0.75

0.47
0.26
0.32
0.43
0.50
0.66
0.27
0.61
0.22
0.43
0.55
0.47
0.36
0.31
0.13
0.30
0.60
0.40
0.37
0.49
0.45
0.27
0.70
0.44
0.69

0.74

0.79

0.70

0.71

0.64

0.69

0.64

0.57

0.51
0.77
0.67
0.59
0.54
0.29
0.68
0.61
0.40
0.39
0.69
0.49
0.41
0.74
0.31
0.73
0.56

0.50
0.79
0.67
0.58
0.61
0.45
0.70
0.59
0.52
0.58
0.63
0.52
0.67
0.77
0.61
0.79
0.63

0.41
0.70
0.59
0.43
0.49
0.49
0.55
0.39
0.45
0.31
0.38
0.21
0.46
0.66
0.28
0.76
0.47

0.42
0.75
0.65
0.52
0.51
0.48
0.60
0.53
0.35
0.50
0.48
0.33
0.43
0.74
0.37
0.78
0.53

0.33
0.57
0.45
0.32
0.39
0.22
0.49
0.22
0.26
0.50
0.48
0.48
0.46
0.63
0.28
0.64
0.44

0.45
0.75
0.68
0.53
0.38
0.39
0.58
0.46
0.22
0.55
0.56
0.39
0.38
0.68
0.51
0.74
0.52

0.46
0.68
0.27
0.27
0.48
0.10
0.15
0.04
0.34
0.30
0.24
-0.03
0.37
0.67
0.43
0.69
0.40

0.43
0.75
0.54
0.45
0.42
0.31
0.50
0.36
0.34
0.29
0.34
0.25
0.39
0.66
0.39
0.67
0.44

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Impression Estimation of Suit Patterns

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the viewing distance and the minimum resolution of the human eye. Suppose aperson's visual acuity is 1.0. In that case, the minimum resolution of the human
eye is approximately 1/60 degree in terms of visual angle, and the resolution atwhich a human can look is calculated from Eq. (2).

dpi =

2.54
2 × d × tan(v1 /2)(2)

Where d is the viewing distance and v1 is the viewing angle (1/60 degree). In thisstudy, we use Eq. (1) and Eq. (2) to compute the range of resolution over which
a person sees a suit pattern. One obtains the nest resolution is when looking at
the fabric up close. At this time, assuming the normally quoted viewing distanceof 57cm, the resolution was about 153dpi, calculated from Eq. (2). Next, one
obtains the coarsest resolution when looking at the entire suit jacket. Here, theeective viewing angle for a person is assumed to be 30 degrees, which is generally
dened. The visual distance for observing the suit pattern was obtained from
Eq. (1), used the actual size of the suit as 75 cm (average value). As a result,the viewing distance for the suit was 140 cm. Calculations using Eq. (2) basedon the calculated viewing distance indicate a resolution of approximately 62dpi.
Substituting this into the constructed impression estimation models, we can
expect the highest estimation accuracy when the impression value of the 72dpi
or 112dpi images is used as ground truth. In this study, the actual size of thesuit fabric was small, making it dicult to capture images at a resolution of
72dpi or lower. Table 2 shows the estimation accuracy at each resolution. The
average correlation coecient for all evaluation words is highest when 72dpi isused as ground truth, and accuracy tends to decrease as resolution increases.
These results suggest that the relationship between the resolution at which one
look at the suit patterns and the optimal resolution in the impression estimation models is close. This conrms that the relationship between the range ofresolution at which people actually see the pattern of the suit and the resolution
in the impression estimation model is a close result. Looking at the evaluation
terms that dier in accuracy between 72dpi and 448dpi, massive and lightfeeling are listed. Both impressions expressed weight-lightness, suggesting amore macroscopic observation of the pattern in these evaluation terms. Furthermore, it was suggested that the aective texture may change depending on thepattern's scale.

6.3 Conrmation of estimation results
Visually conrm the relationship between the estimation results by the constructed impression estimation models and the pattern image of the suit. We
show the top and bottom two suit pattern images with the highest estimation
accuracy (Fig. 6) for each resolution. The evaluation words for the estimation
results are four impression words, simple, unique, vibrant, and calm, andtwo emotion words, deactivation - activation and dislike-like. The models withthe highest accuracy in cross-validation were used for estimation.

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In the simple category, the top patterns primarily were solid black or grayand pinstripe patterns (thin stripes), while the bottom patterns were often loudlycolored stripes and staggered plaid patterns. In the unique category, the top
patterns were often loudly colored stripes and staggered plaid, while the bottom
patterns were standard patterns such as plain black or gray and stripes. In thevibrant category, the top patterns were often brightly colored solid colors andstripes with blue as the base color, while the lower patterns were often plain
colors and stripes of subdued colors. In the calm category, the top patterns forcalmness were plain black or gray and shadow stripe patterns, while the bottom
patterns were brightly colored stripes and staggered lattices. In the deactivation- activation category, most activation patterns are brightly colored stripes or
staggered plaid, while deactivation patterns are calmly colored, such as plain grayor black. In the dislike-like category, standard patterns dominated responses,
such as pinstripes and solid colors for the like response. In contrast, patterns
with gaudy colors and geometric patterns were selected for the dislike response.
These results conrm that the estimation results are close to the human image.

6.4 Relationship between resolution and pattern motifs
Based on the constructed impression estimation model, we discuss the relationship between resolution and pattern motifs. Using the actual impression values
and the model-estimated impression values, we calculate the correlation coecient for each pattern, and visually conrm the pattern with the highest correlation. We do so at each resolution. Fig. 7 shows some of the patterns with highcorrelation coecients (estimation accuracy) at each resolution.
From Fig. 7, patterns with large motifs, such as alternate stripes and solid
colors, are more common at 72 dpi. Alternate stripes are stripes with alternatinglines of dierent colors and are believed to have a larger motif than a typical
stripe pattern. At 112 dpi, there are medium-patterned stripes with mediumsized motifs, such as chalk stripes. On the other hand, patterns with large plainlike motifs are also mixed. Similarly, 224dpi has many medium-sized stripes withmedium-sized motifs, such as pinstripes. At 448dpi, patterns with small motifs,
such as staggered plaid and dot patterns, were observed. These results suggeststhat people change their resolution (viewing angle) according to the motif 's size
and that the aective texture obtained from the pattern may depend on the
motif 's size.

7 Conclusion
In this study, we proposed a method for estimating aective texture that thesuit patterns evoke in response to dierences in pattern scale and conducted thefollowing procedure. (1) collect pattern images of suits at dierent resolutions,and (2) quantify the aective texture of 44 evaluation words through an impression evaluation experiment. (3) Style features extracted from an image are usedas physical characteristics, and the relationship between aective texture and- 565 -

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Impression Estimation of Suit Patterns

evaluation
word

72dpi

112dpi224dpi448dpihighersimplelower

higheruniquelower

highervibrant

lower

higher

calm

lower

higherdeactivation–
activation
lower

higherdislike-likelower

Fig. 6. Estimation results from impression estimation models.

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14

E. Tsumura et al.

72dpi

112dpi224dpi448dpiFig. 7. Suit patterns with high estimation accuracy at each resolution

physical characteristics is modeled using a multi-scale CNN. A 10-fraction crossvalidation was performed, and the correlation coecients between the impression
values of the test data and those estimated by the models were calculated. Results showed moderate or higher positive correlations for most evaluation terms.
We found that multi-scale CNN improves the accuracy compared to single-scale
CNN, conrming the eectiveness of this method. The resolution at which thesuit patterns are viewed from the human visual angle was calculated and compared with the results of the constructed impression estimation models. Theyconrmed that the estimation accuracy was highest for images at 72dpi, close to
the human resolution.
Future work, we expect to improve the estimation accuracy given the small
dataset in this training by increasing the training data using GAN [18].

Acknowledgements This work was supported by JST COI Grant Number
JPMJCE1314.

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