Content uploaded by Jinlian Hu

Author content

All content in this area was uploaded by Jinlian Hu on Jan 29, 2015

Content may be subject to copyright.

978-1-4244-5961-2/10/$26.00 ©2010 IEEE 417

2010 Sixth International Conference on Natural Computation (ICNC 2010)

Neural Network Modeling of Polar Fleece

Fabric Appearance Evaluation

Binjie Xin

Fashion College

Shanghai University of Engineering Science

Shanghai, 201620, China

Jinlian Hu

Institute of Textiles and Clothing

The Hong Kong Polytechnic University

Hung Hom, Kowloon, Hong Kong

Xiaoxia Liu, Lantian Lin

Fashion College

Shanghai University of Engineering Science

Shanghai, 201620, China

Abstract — This paper presents the development of a fairly new

neural network based method that aims at characterizing polar

fleece fabric appearance for the purpose of objective quality

evaluation. Co-occurrence matrix analysis is used to give the

quantitative descriptions of fabric appearance properties; neural

network model is used to establish the relationship between these

essential features and the final rating grade of fabric appearance.

The experimental results demonstrate that good correlation can

be achieved between the actual rating grade and the predicted

rating grade and reveals the possibility of the development of

artificial intelligence system to simulate the functions of human

eyes and brain.

Keywords-Neural Network; Co-occurrence Matrix; Appearance;

Polar Fleece Fabric;

I. INTRODUCTION

Polar fleece is a soft napped insulating synthetic wool

fabric made from Polyethylene terephthalate (PET) or other

synthetic fibers. Fleece has some of wool's finest qualities but

weighs a fraction of the lightest available woolens. Polar fleece

designates fabric with very high specific volume, which makes

fabric soft, lightweight, warm and comfortable. It is

hydrophobic, holding less than 1% of its weight in water, it

retains much of its insulating powers even when wet, and it is

highly breathable. These qualities make it useful for making

clothing intended to be used during strenuous physical activity.

However, the fibers on the surface of the polar fleece tend to

bunch up and form regular beards on the fabric surface after

wearing or washing. As an important factor of polar fleece

fabric quality, evaluating wearing appearance of polar fleece

fabrics objectively, quickly and reliably becomes urgent for

both the purchaser and the supplier to achieve quality

agreement with the market expanding.

Traditionally, evaluating fabric appearance still depends on

the human eyes and brain and thus suffers the drawbacks of

reliability due to human fatigue, speed, and overall accuracy.

These years, image analysis techniques, instead of human eyes

and brain, have been applied to textile manufacturing and

inspection of textile surface characteristics [1-3], analysis of

cotton fiber maturity [4], characterization of nonwoven

structures [5,6], and evaluation of carpet aesthetic appearance

[7,8].

In this paper, we establish one set of objective evaluation

system based on the co-occurrence matrix analysis and neural

network method for the evaluation of polar fleece fabric

appearance after abrasion. Co-occurrence matrix is used to

characterize the appearance properties of polar fleece fabric

and BP neural network is used for the grade rating as artificial

intelligent classifier.

II. DATA ACQUISITION

Grade1 Grade2 Grade3

418

Grade 4 Grade 5

Figure 1. Standard Polar Fleece Appearance Photographs

According to the comfortable feeling of visual perception

when evaluating polar fleece appearance, we divide polar

fleece fabrics into five grades as illustrated in Figure 1. Grade

five is the most comfortable, which give a satisfied aesthetic

feeling. Grade one is the worst; it cannot satisfy the basic

aesthetic feeling requirement of consumers. It is obvious that

texture properties such as uniform, roughness and regularity

are the most important features for the quality evaluation. The

investigation of this phenomenon has been conducted in our

previous research papers. As the co-occurrence matrix is an

effective method for the characterization of texture image, it is

utilized in this paper to give a quantitative description of the

polar fleece fabric after abrasion.

A. System set-up

Figure 2. Digital System for the Fabric Appearance Evaluation

As illustrated in Figure 2, the instrument setup consists of a

digital camera to capture the image of the mounted polar

fleece fabric samples on the sample platform, one set of

lighting sources to illuminate the fabric sample at a low angle,

one enclosure box containing the fixed sample platform and

the lighting sources, a computer connected to the digital

camera for image data collection and further data analysis. The

digital camera is placed above the lighting box and mounted at

a distance sufficient to capture a well focused image of fabric

sample.

B. Sample Specification

In our research, 15 kinds of commercially available polar

fleece fabrics with different solid colors and different pilling

resistant abilities were used as samples. Each fabric sample

was cut into approximately 105mm (43/16 in.) square and

subjected to laboratory abrasion in a Random Tumble Piling

Tester for half an hour according to ASTM Test Method

D3512.

The image resolution of the digital camera used here is 1600

by1200 pixels. In this study, we cropped each fabric image

into size 512 by 512 pixels; each pixel has values of 256 gray

levels, with the zero value representing black and 255

representing white. After digitalization of polar fleece fabric

appearance image, a set of algorithms for the calculation of

Co-occurrence matrix parameters will be developed in section

III.

III. CO-OCCURRENCE MATRIX ANALYSIS

A. Basic Principle of Co-Occurrence Matrix[9,10]

Grayscale Co-occurrence matrix is a widely applied method

for texture analysis, whose indicators are usually used as

feature parameters for such analysis. Grayscale Co-occurrence

matrix contains valuable spatial organization information of

texture. A regular sharp texture is reflected by accrete matrix

concentrated around the diagonal, while a scattered Co-

occurrence matrix is an indication of irregular and fuzzy

texture. If the texture shows highly uniform orientation and the

displacement vector is consistent with texture orientation, the

accrete matrix would be on the main diagonal.

Assuming that the size of image ),( yxf is yx NN × and

grayscale is g

N, the distance between two random pixels

),(),,( nmkj and position angle

θ

should be:

dnkdmj

dnkdmjdR

dnkmjdR

dnkdmj

dnkdmjdR

nkdmjdR

LD

V

RD

H

−=−=−

=−−=−°=

=−=−°=

−=−−=−

=−=−°=

=−=−°=

,

,,:)(135

,0:)(90

,

,,:)(45

0,:)(0

θ

θ

θ

θ

(1)

Times of occurrence of grayscale pair ),( qp in Co-

occurrence matrix are:

q}p,f(m,n)(d),f(j,k)m,n))||R#{((j,k),()5P(p,q,d,13

q}p,f(m,n)(d),f(j,k)m,n))||R#{((j,k),()P(p,q,d,90

q}p,f(m,n)(d),f(j,k)m,n))||R#{((j,k),()P(p,q,d,45

q}p,f(m,n)(d),f(j,k)m,n))||R#{((j,k),()P(p,q,d,0

RD

V

LD

H

===°

===°

===°

===°

(2)

4

1

2

3 3

5

1 CCD Camera

2 Lighting Box

3 Lighting Source

4 Sample platform

5 Com

p

ute

r

419

Hereinto, # stands for number of elements in the

aggregation. As Co-occurrence matrix is symmetric matrix,

thus ),,,(),,,(

θθ

dijPdjiP =.

Figure2 demonstrates the co-occurrence matrix of five

standard polar fleece appearance photographs by the means of

2-D gray scale image, gray level represents the frequency of

occurrence of grayscale pair.

Grade5(θ=0,d=1) Grade4(θ=0,d=1) Grade3(θ=0,d=1)

Grade 2(θ=0,d=1) Grade 1(θ=0,d=1)

Figure 3. Co-occurrence matrix of five standard images

B. Feature Parameters in Co-Occurrence Matrix

The Co-Occurrence matrix is normalized for the

convenience of feature parameter definition before the

specification of features.

• Texture uniformity (Q1):

∑∑

=

==

gg

N

i

N

j

jipQ

00

2

1),( (3)

Uniformity (Q1) reflects the homogeneity of image’s

texture. A higher value of uniformity indicates a more

consistent texture.

• Texture contrast (Q2):

∑

⎪

⎭

⎪

⎬

⎫

⎪

⎩

⎪

⎨

⎧

∑∑

=

−

=

=−

==

1

000

2

2),(

ggg

N

n

nji

N

i

N

j

jipnQ (4)

Contrast (Q2) reflects the diversity of image’s texture. A

low value of Q2 indicates coarse texture.

• Texture entropy (Q3):

∑∑

−=

==

gg

N

i

N

j

jipjipQ

00

3)},(log{),( (5)

An ordered distribution of Co-occurrence matrix

corresponds with maximum value of entropy. A higher value of

entropy indicates better regularity in texture.

• Texture correlation (Q4):

yx

N

i

N

jyx

gg

uujipij

Q

σσ

∑∑ −

===00

4

),()(

(6)

Hereinto, yxyx uu

σσ

,,

,is mean value and variance

of yx pp ,, ∑

=

=

g

N

i

xjipp

0

),(, ∑

=

=

g

N

j

yjipp

0

),(.

Correlation coefficient of texture reflects the correlations

between textures in different orientations. Coarse texture shows

a higher correlation coefficient value than fine texture.

TABLE I. PARAMETERS OF CO-OCCURRENCE MATRIX

Grade Q1

(θ=0,d=1)

Q2

(θ=0,d=1)

Q3

(θ=0,d=1)

Q4

(θ=0,d=1)

1 0.079135 45.389 0.0292 -3.1401

2 0.051062 109.06 0.0839 -2.3635

3 0.039969 169.06 0.0927 -2.1544

4 0.024251 264.54 0.1129 -1.1701

5 0.012324 332.95 0.1381 -0.8302

IV. NEURAL NETWORK MODELING

Artificial Neural Networks (ANN) [11-13] have been applied

to different textile problems such as classifying patterns or

defects in textile textures, and predicting fabric parameters

[14-15]. Among many Artificial Neural Network schemes,

feed-forward neural networks with back-propagation learning

algorithms based on gradient descent have been widely used,

since they offer unlimited approximation power for non-linear

mappings. Therefore, we establish an ANN model based on

feed-forward back propagation network and investigate the

performance of its neurons function to simulate the human

judging behavior;

According to the essential texture features based on Co-

occurrence matrix analysis, a two layer ANN system with 4

inputs and 1 output as shown in Figure 3, four input variables

include Q1, Q2, Q3 and Q4, one output variable is the final

rating grade. To be more explicit, a two-layer feed forward

NN model consists of a stream of input vector X of 4 neurons,

i.e. },,,{ 4321 xxxx , to the input layer, and an output layer with

a single neuron. The network has the general form:

⎭

⎬

⎫

⎩

⎨

⎧∑++=Θ=

=

)(),(

0

10

n

iiii bxwfbfXfY (7)

where Yrepresents the generated output response variable;

Θdenotes the overall parameters space; i

wdenotes the

connecting weight of the ith input neuron; 0

b and i

b are the

bias nodes; 1

fand fare the activation functions of first layer

and second layer respectively.

420

Figure 4. Design of Neural Network

Here, we use LOGSIG as the activation function of four

neurons of first layer and PURELIN as the activation function

of second layer.

;)(

;)(

1xx

xx

ee

ee

xf

xxf

−

−

+

−

=

=

(8)

V. SYSTEM TRAINING

The neural network model for the fabric appearance evaluation

was trained using a training set that includes 50 samples

ranging from grade 1 to grade 5. The training process was

terminated at a point where all the samples in this training set

were correctly identified. We first input the training data to the

network and perform gradient descent using the Levenberg-

Marquardt Algorithm. Parameters are adjusted iteratively until

global error function converges to some specified minimum so

that good generalization is achieved. Some important

considerations are the number of training iterations, the

learning rate and the momentum coefficient.

Supposing the estimated grade y is governed by the underlying

function );( *

θ

xf , where

x

is the set of input variables and *

θ

represents the true values of the parameter vector

θ

from the

parameter space Θfor the function which models the process.

With nobservations, the simulation process is represented by:

Θ∈=+= ** ,~1,);(

θεθ

nixfy iii (9)

The least-square estimate of *

θ

is the

θ

, which is obtained by

minimizing the error function:

∑−=

=

n

iii xfyS

1

2

)];([)(

θθ

(10)

The Levenberg-Marquardt algorithm uses this approximation

to the Hessian matrix in the following Newton-like update:

eJJJ TT

kk

1

1][ −

+Ι+−=

μθθ

(11)

Where, Jis the Jacobian matrix that contains first derivatives

of the network errors with respect to the weights and biases,

and e is a vector of network errors. When the scalar µ is zero,

this is just Newton's method, using the approximate Hessian

matrix. When µ is large, this becomes gradient descent with a

small step size. Newton's method is faster and more accurate

near an error minimum, so the aim is to shift towards Newton's

method as quickly as possible. Thus, µ is decreased after each

successful step (reduction in performance function) and is

increased only when a tentative step would increase the

performance function. In this way, the performance function

will always be reduced at each iteration of the algorithm. For

this study, we used the mean square error (MSE) between the

predicted output of the model and the target actual value as the

following equation:

n

TY

MSE

n

iii

2

1

)(

∑−

== (12)

Where n is the number of training data set. We trained the

neural network model using the parameters extracted using co-

occurrence matrix, and the MSE is 0.002624.

Figure 5. Performance of the model on the training data set

Figure 6. The Training Performance of Neural Network Model

VI. VALIDATION

After we trained the neural network models, the neural

network system was tested using the validation data set of 50

samples. The experimental results show that the artificial

intelligence system developed in this project could achieve a

good consistent with the human evaluation results, the

predicted rating grade tracked the actual rating grades very

421

well, as the correlation coefficient was sufficiently high (R2 =

0.9828).

Figure 7. Validation of Neural Network Model

VII. CONCLUSION

An artificial intelligence experts system based on image

analysis and neural network method was developed for the

objective evaluation of polar fleece fabric appearance. Co-

occurrence matrix was used to characterize the polar fleece

fabric appearance and one set of two-layer BP neural network

model based was established for rating of the degree of polar

fleece fabric appearance. The testing results for validation

show that the artificial intelligent system developed in this

research has very good correlationship with the human being’s

behaviour. It proves that it is workable to replace the

traditional subjective evaluation method based on human eyes

and brain by using the modern artificial intelligence method.

ACKNOWLEDGMENT

The first author of this paper would like to thank the Hong

Kong Polytechnic University for supporting him to work at the

Institute of Textile and Clothing and Department of Computing

during of his PhD studies.

REFERENCES

[1] J.Amirbayat and M.J.Alagha, Objective Assessment of Wrinkle

Recovery by Means of Laser Triangulation, J.Textile Inst., 1996, Vol.87,

Part I, No.2, 349-354.

[2] B.Xu and D.F.Cuminato, N.M.Keyes, Evaluating Fabric Smoothness

Appearance with a Laser Profilometer, Textile Res. J. 68(12), 900-906,

1998.

[3] Z. Fazekas, J.Komilves, I. Renyi and L. Surjan, Towards Objective

Visual Assessment of Fabric Features, Image Processing and its

applications, Conference Publication No.465 ©IEE 1999,411-416.

[4] D.D.Thibodeaux, Cotton Fiber Maturity by Image Analysis, Textile

Res.J，1986,VoL 56,130~139

[5] R.H.Gong and A.Newton, Image-analysis Techniques, Part II: The

Measurement of Fiber Orientation in Nonwoven Fabrics,

J.Text.Inst.,1996, 87, No 2, 371-388.

[6] F.Thorr, J.Y.Drean, and D.Adolphe, Image Analysis Tools to Study

Nonwovens, Textile Res. J. 69 (3),162-168 (1999).

[7] B. Pourdeyhimi, B. Xu, and J. Sobus,, Evaluating Carpet Appearance

Loss: Surface Intensity and Roughness, Textile Res. J. 63(9), 523-

535(1993).

[8] B. Pourdeyhimi, B. Xu, and L. Wehrel, , Evaluating Carpet Appearance

Loss: Periodicity and Tuft Placement, Textile Res. J. 64(1), 21-22(1994).

[9] M. Robert, K.H. Shanmugam, Textural Features for Image

Classification, IEEE TRANSAXTIONS ON SYSTEMS, MAN, AND

CYBERNETICS, 1973, SMC-3(6): p. 610-621.

[10] L.S.Davis, S.A.J., J.K. Aggarwal, Texture Analysis Using Generalized

Co-Occurrence Matrixes, IEEE TRANSAXTIONS ON PATTERN

ANALYSISAND MACHINE INTELLIGENCE, 1979, PAMI-1(3): p.

251-259.

[11] P. Lipmann, an Introduction to Computing with Neural Nets, IEEE

ASSP Magazine, April, 1987.

[12] J.E. Dayhoff, Neural Network Architectures: An Introduction, Van

Nostrand Reinhold, USA, 1990.

[13] L. Fu, Neural Networks in Computer Intelligence, McGraw-Hill,

Singapore, 1994.

[14] P. Chen, T. Liang, etc., ‘Classifying Textile Faults with a Back-

Propagation Network using Power Spectra’, Textile Research Journal,

68(2), 121-126,(1998).

[15] I.S. Tsai, C. Lin and J. Lin, ‘Applying an Artificial Neural Network to

Pattern Recognition in Fabric Defects’, Textile Research Journal, 65(3),

123-130,(1995).