Supervised FCNN Classification for Garment Seams

Yonghui Pan1,2 1School of Information Technology, Southern

Yangtze University No. 1800, Lihudadao, Wuxi

Jiangsu, China 2Jiangyin Polytechnic

College pyh.1972@yahoo.com.cn

Fang Bao1,2 1School of Information Technology, Southern

Yangtze University No. 1800, Lihudadao, Wuxi

Jiangsu, China 2Jiangyin Polytechnic

College baofang@mail.jypc.org

Shitong Wang School of Information Technology, Southern

Yangtze University No. 1800, Lihudadao, Wuxi

Jiangsu, China wxwangst@yahoo.com.cn

Abstract

In this paper, a supervised fuzzy clustering neural network (SFCNN) is introduced for constructing the garment seam evaluation system. Our experimental results demonstrate that the proposed system could efficiently be used as an objective garment seam evaluation system with high accuracy and is robust for various structures and mechanical properties of middle-thickness cotton fabric. 1. Introduction

AATCC (American Association of Textile Chemists and Colorists) Method 88B has been commonly used for the subjective evaluation of seam pucker. According to this method, the appearance of seams are compared with photographic standards and the severity of seam pucker is graded into five classes, Class 5 being little or no pucker, and Class 1 severe pucker. Normally, Class 5 and 4 are acceptable, Class 3 is critical or borderline, and Class 2 and 1 are unacceptable. The merit of this method is directness, simple, low investment and easy to master, but it is influenced by uncertain factors of evaluating people and loses it’s objectivity easily.

Recently, many methods have been developed for objective evaluation of seam pucker. Among these methods, the relatively matured are listed as follows: J. Amirbayat uses strain of sewing thickness to evaluate seam pucker objectively [1]. A. M. Manich applies multi-regression to establish the sewing index prediction model for pure wool and blend fabric [2]. G. Stylios uses image processing technology to evaluate seam pucker of fabric quantitatively by extracting the

sewing factors of fabric gray images [3]. C. K. Park employs three dimensional imaging analysis technology and artificial intelligence technology to evaluate seam pucker through five wrinkle shape parameters [4]. J. Fan proposes an objective evaluation system with three-dimensional laser scanning technology to evaluate seam pucker [5].

Applying main factor analysis to the mechanical properties of middle thickness cotton fabric under low stress, we propose an objective evaluation method based on SFCNN (supervised fuzzy clustering neural network). This paper is organized as follows. Section 2 presents the individual steps of our approach for SFCNN. Section 3 discusses the experiment results. In the final section conclusions are given. 2. Supervised FCNN 2.1. FCM clustering

The Fuzzy c-means (FCM) clustering algorithm is a set-partitioning method based on Picard iteration through necessary conditions for optimizing a weighted sum of squared errors objective function mJ . Let 2≥c be an integer; let n

N RX ⊂= ),,( 1 xx be a finite data set containing at least Nc < distinct points; and let cNR denote the set of all real Nc × matrices. A non-degenerate fuzzy c-partition of x is conveniently represented by a matrix cN

ik RuU ∈= ][ , the entries of which satisfy:

]1,0[∈iku , ci ≤≤1 , Nk ≤≤1 (1)

NuN

kik <<∑

=1

0 , ci ≤≤1 (2)

2007 International Conference on Computational Intelligence and Security

0-7695-3072-9/07 $25.00 © 2007 IEEEDOI 10.1109/CIS.2007.88

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2007 International Conference on Computational Intelligence and Security

0-7695-3072-9/07 $25.00 © 2007 IEEEDOI 10.1109/CIS.2007.88

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2007 International Conference on Computational Intelligence and Security

0-7695-3072-9/07 $25.00 © 2007 IEEEDOI 10.1109/CIS.2007.88

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2007 International Conference on Computational Intelligence and Security

0-7695-3072-9/07 $25.00 © 2007 IEEEDOI 10.1109/CIS.2007.88

555

2007 International Conference on Computational Intelligence and Security

0-7695-3072-9/07 $25.00 © 2007 IEEEDOI 10.1109/CIS.2007.88

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2007 International Conference on Computational Intelligence and Security

0-7695-3072-9/07 $25.00 © 2007 IEEEDOI 10.1109/CIS.2007.88

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The FCM algorithm was developed to minimize the objective function

∑∑= =

=N

k

c

iik

mikm duJ

1 1

),()( vx , ∞<< m1 (3)

In formula (3), ),( ikd vx is any inner product norm metric of the distance between the feature vector

Xk ∈x and the prototype ni R∈v . A metric often used

in applications is the squared Euclidean distance between

kx and iv , that is 2),( ikikd vxvx −= . The

coupled first order necessary conditions for solutions ( VU , ) to min{ }),( VUJm

are

∑=

−

−

−=

c

j

m

jk

ik

iku

1

))1/(2(

1

vx

vx

(4)

∑∑

=

== N

kmik

N

k kmik

iu

u

1

1x

v ， ci ≤≤1 (5)

2.2. SFCM Clustering

In this section, we extend the original FCM objective function used by linear summation sub-networks and propose a supervised fuzzy c-means clustering (SFCM) model. Rather than defining

mJ based on n

k R∈x only, we supply it with information on the output space by defining a new objective function which assumes the following form:

∑∑= =

−+−=N

k

c

i

ikikkik

mikm uuJ

1 1

2 ])[( yyvx (6)

where ky and i

ky are the corresponding desired output of SFCNN and computing output of sub-networks (in fig. 1) respectively. The first term of formula (6) denotes fuzzy c-partitions of input patterns

kx by minimizing the distance between inputs kx and

prototypes iv . The second term of formula (6) requests the computing output of system to approach the desired output mostly.

Now, by applying the Lagrange multipliers technique to formula (6), we derive the necessary conditions for the partition matrix and the prototypes, namely

∑=

−

−

−

−

=c

i

m

ikik

m

ikik

iku

1

1/12

1/12

vxy

vxy , ci ≤≤1 , Nk ≤≤1

(7)

∑∑

=

== N

km

ik

N

k km

iki

u

u

1

1

)(

)( xv , ci ≤≤1 (8)

The demonstration of formula (7) and (8) refers to reference [6]. 2.3. Structure of SFCNN

The structure of SFCNN is shown in Fig. 1 [7, 8].

1:networkSub −

tionClassificaSFCM

2:networkSub −

cnetworkSub :−kx

ky

1ky

2ky

cky

ku1ku2cku

Fig. 1. Construction of SFCNN There are two parts in SFCNN. One is supervised

fuzzy-c means classification, and the other is linear summation sub-networks [6].

Let nN R⊂},,{ 1 xx be the patterns to cluster and

qk R∈y , Nk ,,1= be the corresponding desired

output. Suppose to organize the data in c clusters, we can associate a local linear summation model for each cluster. The topology of the linear summation sub-network is shown in Fig. 3.

2kx knx

kiy 1

kiy 2

kiqy

iw11

iw21iqw 1

iw12

iw22iqw 2

inw1

inw2 i

qnw

1kx

Fig. 2. Topology of sub-network Where Ti

qii

i ),,,( 21 wwww = ,

),,,( 10ijn

ij

ijij www=w , and the output of ith sub-

network and SFCNN is ki

ik xwy = and

kiikk u xwy = respectively.

2.4. Calculation weights of sub-networks

In order to calculate the weights of the ith sub-network n

i R∈w , ci ,,1= , we solve c least square problems [9], that is, one for each sub-network:

556556556556556556

{ }∑∑==

−=−=N

kkkiik

N

kk

ikik uuE

1

2

1

2

21}{

21min yxwyy (9)

We can rewrite this expression in the form

{ }∑=

−=N

kkkiiE

1

2)(21min yxw φ (10)

Where kikki u xx =)(φ ,ky is the corresponding

desired output of SFCNN. Differentiating formula (10) with respect to the

weights iw and setting the derivative to zero we obtain

{ } 0)()(1

=−=∂∂ ∑

=

N

kkikkjj

i

V xyxww

φφ , cj ,,1= (11)

Writing formula (11) in matrix notation we have YΦWΦΦ TTT =)( , and then

YΦW T ∧= (12) Where ∧Φ denote the pseudo-inverse of matrices

Φ . 2.5. Algorithm for SFCNN training

The algorithm is composed of the following steps (see Fig. 3).

{

{{

]1,0[,0,0::.1

randomuclset

ik ==>ε

)12(),8(:,:.2 1)1(

formulacomputing l

il

i++ wv

)7(::.3 )(

formulauupdating l

ik

)9(:,.4 formulaE ε<

iikuoutputnterminatioalgorithm

w,:.5 −

tionInitializa

Iteration

nTerminatio

No

Yes

Fig. 3 Steps of SFCNN training algorithm

3. Experimental results

In this paper, we apply FAST (Fabric Assurance by Simple Testing) system to test the low stress mechanical properties of middle-thickness cotton fabric. The FAST system is simple to operate to predict the formability, machinability of fabric, dimensional stability of garment with the use of fingerprint chart. And the prediction can give constructive suggestions and manufacture instructions to garment manufacturer and fabric factory respectively. We choose 28 pieces of middle-thickness cotton fabric as test samples. The

basic specifications of the test samples are shown in table 1.

Table 1. Basic parameters of test samples

sample ends per inch

picks per inch

fabric weave

fabric weight (g/m2)

fabric thickness

(mm) middle

-thickness cotton fabric

53.3-133.3 40.6-113.8 plain; twill 108-232 0.274-0.756

The test samples are 30cm×60cm sewing threads. Both the above and nether sewing threads have been sewed in the middle along the same fabric grain direction. Because the direction of fabric cutting piece must be always the same in most cases, we only consider the seam pucker grade when the direction of both the above and nether cutting piece is the same. In this paper, we pay more attention to the influence of mechanical properties on the sewability of fabric but not to the influence of sewing conditions. According to the practical manufacture experience, we impose corresponding sewing conditions to make the different sewing threads. The sewing samples have been sewed by skilled workers using suitable flat sewing machine. The sewing conditions are shown in the Table 2. The sewing threads which have been washed and aired well are evaluated with AATCC-88B criterion to get corresponding seam pucker grade by two experts.

Table 2. Specification of needle and thread needle size (unit style)

thread type (tex)

stitch length (3cm)

11 7.3×3 12-15

Evaluating the sewability along five different directions, we measure and compute the corresponding mechanical properties of each fabric, and get 140 of groups experiment data. Thereinto, 110 groups of data are used to training neural network, and the rest 30 groups are used to simulate. According to Pearson rank-order correlation method, we find great correlation among these mechanical properties. So we remove those properties which are highly correlated with each other and those scarcely related to fabric sewability, and apply main factor method to analyze the reserved factors to get rid of the redundant information [10]. At last, we get six simple and effective main factors as the input of the SFCNN. And the seam pucker grade of AATCC-88B is as the output of neural network. The definitions of six main factors are shown in Table 3.

Table 3. Definitions of main factors main factor relative index

1 moldability warp bending rigidity; weft bending

rigidity; 45 º diagonal bending rigidity; warp formability; weft formability

2 heavy fabric thickness under 2gf; fabric thickness

557557557557557557

under 100gf; fabric surface thickness; fabric weight (g/m2)

3 warp action warp tensile strength under 5gf/cm; warp tensile strength under 20gf/cm; shearing rigidity

4 45º diagonal action diagonal tensile strength under 5gf/cm;

diagonal tensile strength under 20gf/cm; weftformability

5 weft action weft tensile strength under 5gf/cm; weft tensile strength under 20gf/cm

6 dimensional stability warp relaxation shrinkage; weft relaxation shrinkage

The above mentioned six main factors are

orthogonal and independent. According to weight coefficients of the original variable in main factors, we can obtain corresponding dimensionless computing value of each main factor.

Due to the number of the main factors and the seam pucker grade of subjective evaluation has been fixed, we set the corresponding node number of the input layer and output layer of SFCNN is 6 and 1 respectively. Also we set the corresponding node number of the first layer and second layer of each sub-network (see Fig. 2) is 6 and 3 respectively, and let the fuzzy weight coefficient of supervised FCM clustering m=2. Reference [11] uses ordinary BP neural network to evaluate seam pucker, and sets the number of hidden layer equal to 6 and the objective error equal to 0.01. The optimizing iteration number of BP network is about 26000. In this paper, we apply SFCNN to evaluate seam pucker. By large numbers of testing, we find the convergence speed of our algorithm is very fast on the same objective error 0.01, and the iteration number of SFCNN training is about 104 degree. The 30 groups of testing data in table 4 is the prediction results of seam pucker grade which are evaluated by the ordinary BP network (reference [11]) and SFCNN respectively.

Table 4. Predicted of BP network and SFCNN ordinary BP network SFCNN

samples subjective evaluation objective

evaluation relative error

objective evaluation

relative error

1# 3.50 3.17 -9.43% 3.66 4.57%

2# 4.50 4.09 -9.11% 4.33 -3.78%

3# 3.00 2.84 -5.33% 2.89 -3.67%

4# 4.50 4.23 -6.00% 4.58 1.78%

5# 4.50 4.95 10.00% 4.61 2.44%

6# 4.00 3.77 -5.75% 3.89 -2.75%

7# 4.50 4.13 -8.22% 4.37 -2.89%

8# 4.00 4.21 5.25% 4.09 2.25%

9# 4.00 3.98 -0.50% 4.11 2.75%

10# 3.50 3.41 -2.57% 3.47 -0.86%

11# 5.00 4.95 -1.00% 5.12 2.40%

12# 5.00 4.79 -4.20% 4.92 -1.60%

13# 4.50 4.66 3.56% 4.61 2.44%

14# 5.00 5.18 -3.60% 4.81 -3.80%

15# 4.00 4.37 9.25% 4.16 4.00%

16# 4.50 4.43 -1.56% 4.35 -3.33%

17# 4.00 4.23 5.75% 3.90 -2.50%

18# 4.00 4.27 6.75% 4.17 4.25%

19# 3.00 2.86 -4.67% 3.06 2.00%

20# 3.50 3.17 -9.43% 3.64 4.00%

21# 3.50 3.22 -8.00% 3.31 -5.43%

22# 4.00 3.79 -5.25% 4.29 7.25%

23# 5.00 4.75 -5.00% 4.95 -1.00%

24# 3.50 3.62 3.43% 3.38 -3.43%

25# 3.00 2.73 -9.00% 3.16 5.33%

26# 3.00 2.80 -6.67% 3.02 0.67%

27# 3.00 2.96 -1.33% 3.13 4.33%

28# 3.50 3.36 -4.00% 3.44 -1.71%

29# 3.50 3.37 -3.71% 3.51 0.29%

30# 5.00 4.61 -7.80% 4.92 -1.60%

From table 4, we can see the prediction results of SFCNN is more closer to the subjective evaluation values, the biggest relative error is below 7.5%, and the whole prediction accuracy is much better. To quantitatively describe the relativity between the objective evaluation and subjective evaluation, we compute the relative coefficients of the ordinary BP network and SFCNN respectively through the results of table 4. The relative coefficients are shown in Fig. 4.

Fig. 4. Relative coefficients of BP and SFCNN

From Fig. 4, we also can see both two methods have

higher relative coefficient. The correlation coefficients of the ordinary BP network and SFCNN are 94.8% and 98.1% respectively, but the SFCNN have much better prediction capability. 4. Conclusions

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In this paper, we propose an objective seam pucker grade evaluation system based on SFCNN. This system can evaluate the seam appearance grade of fabric fast, effectively and objectively. But with the limitation of our time and energy, the system we proposed still has some deficiencies: The first is the number of fabric samples we used is not abundant. Because the kinds and the structures of cotton fabric are very complicated and various, we can not choose all kinds of fabric as our samples, but some middle-thickness cotton fabric which is often used and has ordinary structure. The system we established only can predict the same or approximate kind of fabric. So how to improve the generalization capability of this objective evaluation system is the important aspect in the future. The second is that more and more manufacturers and traders of cotton fabric want to get helpful suggestions from the fabric sewability prediction software as FAST system becomes more popular. If we can develop corresponding software with kindness interface and powerful database support, our objective evaluation system will be more wildly applied. Things mentioned above will be researched further in the future. 5. References [1] J. Amirbayat, “Seams of different ply properties, Part 1: Seam Appearance, Part 2: Seam Strength”, Journal of the Textile Institute, Vol.82, No.2, 1992, pp.211.

[2] A. M. Manich, and J. P. Domingues, “Relationships between fabric sewability and structural, physical, and FAST properties of woven wool and wool-blend fabrics”, Journal of the Textile Institute, Vol.89, No.3, 1998, pp.579-591. [3] G. Stylios, and J. Sotomio, “A Neural network approach for the optimization of the sewing process of wool and wool mixture fabrics”, Proc. of 1st China International Wool Textile Conference, Xi’an, 1994, pp.689-693. [4] K. P. Chang, and J. K. Tae, “Objective rating of seam pucker using neural networks”, Textile Research Journal, Vol.67, No.7, 1997, pp.494-502. [5] J. Fan, and F. Liu, “Objective evaluation of garment seams using 3D laser scanning Technology”, Textile Research Journal, Vol.70, No.11, 2000, pp.1025-1030. [6] Y. H. Liu, and Q. Liu, “Speech Recognition Based on Fuzzy Clustering Neural Network”, Chinese Journal of Computers, Vol.29, No.10, 2006, pp.1894-1900. [7] U. K. Yi, J. W. Lee and K. R. Baek, “A fuzzy neural network-based decision of road image quality for the extraction lane-related information”, International Journal of Automotive Technology, Vol. 6, No. 1, 2005, pp. 53-63. [8] T. G. Barbounis, and J. B. Theocharis, “A locally recurrent fuzzy neural network with application to wind speed prediction with spatial correlation”, Neurocomputing, Vol. 70, 2007, pp. 1525-1542. [9] A. Staiano, R. Tagliaferri, and W. Pedrycz, “Improving RBF networks performance in regression tasks by means of a supervised fuzzy clustering”, Neurocomputing, Vol. 69, 2006, pp. 1570-1581. [10]Y. L. Hu, and S. H. He, Synthetical evaluation method, Beijing: Scientific Inc. Pub., 2000. [11]K. Liu, Objective evaluation systems of the garment seam pucker grade based on mechanical properties of fabric. Shanghai: Donghua University, 2005.

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