594 TEX11LE RESEARCH JOURNAL

Intelligent Animal Fiber Classification with Artificial Neural Networks

F .H. SHE, L.X. KONG, S. NAHA V ANDI, AND A.Z. KOUZANI

School of Engineering and Technology, Deakin University, Geelong, Victoria 3217, Australia

ABSTRACT

Artificial neural networks (ANN) are increasingly used to solve many problems relatedto pattern recognition and object classification. In this paper, we report on a study usingartificial neural networks to classify two kinds of animal fibers: merino and mohair. Wehave developed two different models, one extracting nine scale parameters with imageprocessing, and the other using an unsupervised artificial neural network to extractfeatures automatically, which are determined in accordance with the complexity of thescale structure and the accuracy of the model. Although the first model can achieve higheraccuracy, it requires more effort for image processing and more prior knowledge, since theaccuracy of the ANN largely depends on the parameters selected. The second model is morerobust than the first, since only raw images are used. Because only ordinary optical imagestaken with a microscope are employed, we can use the approach for many textileapplications without expensive equipment such as scanning electron microscopy.

ters. These classifiers still work with parametric discrimi-nant functions but are able to implement a larger class ofdiscriminant shapes (eventually any shapes, whichmakes them universal approximators) [9].

Supervised artificial neural networks are one of themost exciting semi parametric classifiers. Recently, arti-ficial neural network models and learning algorithms forpattern recognition and classification have been devel-oped. In the current work, we develop an intelligentanimal fiber classifier by integrating image processingand artificial neural networks to classify two kinds ofanimal fibers: mohair and merino.

Scale Patterns of Animal Fibers

Merino and mohair fibers, like other natural animalfibers, consist of three morphological components: thecuticle on the surface, the cortex, and the medulla. Thecuticle is composed of flat, plate-like cells called scales.They are laid down in an overlapping pattem with thefree ends pointing towards the fiber tip. Scale patterns areassociated with aspects of fleece quality and have a greatbearing on the characteristics of products made from thefibers. They also provide very important informationabout the identities of animal fibers in classification.

However, there are considerable variations in the shapeand contour of the scale cells and their arrangement withinthe cuticle. This happens even within the same animal andalong the same fiber because of the nature of growth [I, 2].Furthermore, there are very subtle differences between dif-ferent animal fiber types, but there are still some common

Accurate classification of animal fibers used in thewool industry is very difficult, although a number oftechniques have been developed. Some techniques dis-tinguish these fibers from the patterns of their cuticularscales and others from their physical and chemical prop-erties. However, the characteristic features of thesescales are still the most useful evidence for a skilledmicroscopist to distinguish animal fibers such as merino,mohair, and cashmere [6, 13]. From this point of view,classification of animal fibers is actually a typical task ofpattern recognition and classification.

Recently, to develop an objective method to identifyand subsequently classify animal fibers, Robson used animaging processing technique to extract characteristicinformation from scale patterns and linear demarcationfunctions to classify cashmere and merino fibers basedon a linear discriminant statistical analysis [11]. Thismethod is called a parametric classifier because its dis-criminant functions are based on statistical models andhave a well-defined mathematical functional form (nor-mally Gaussian) that depends on a set of parameters suchas mean and variance. However, some nonparametricclassifiers have no assumed functional form for the dis-criininants. Nonparametric classification is solely drivenby the data. It is free from assumptions about the shapesof discriminant functions or data distributions that maybe erroneous. but this method requires a large number ofdata in order to perform acceptably [10].

There is another, more versatile method called a semi-parametric classifier. which is an excellent compromisebetween versatility and the number of trainable parame-

Textile Res. J. 72(). 594-600 (2002) 0040-5175/$1500

JULY 2002 595

characteristics within the same fiber types. For example,mohair fibers have regular diameters and their scales havemore distinct margins and irregular mosaics, while the scaleedges of merino fibers are more likely to be parallel to eachother and directionally arranged (Figure I) [13].

two different models in the system, and the differencebetween the two models is how the scale features of theanimal fibers are extracted. There are five steps in theclassification system: sampling, image capture, imageprocessing and/or nonnalization, feature extraction, andfiber classification. The first two steps are the same forboth models.

SAMPLING

To prevent superficial scale patterns of animal fibersfrom being blurred by transverse markings raised fromscale edges on the under surface, we captured cast im-ages of fibers by optical microscopy. To make the casts,fiber specimens were mounted on microscope slides invarious media. Provided laboratory manipulation is skill-ful, the surface-scale patterns of the fibers can be faith-fully reproduced [13]. The best mounting agent for gen-eral work is medical-grade white mineral oil or colorlessnail polish of good quality. No swelling occurs duringthe mounting process, and any mount so made can beused for identification purposes. We used Orly@, a highquality nail varnish as the mounting medium to obtainstatic cast images of these fibers. In this way, 537 fibersamples randomly taken from different ranges of sourceswere prepared as slides. These samples consisted of 269merino and 268 mohair fibers.

(a) Merino (400 x) (b) Mohair (400 x)

FIGURE 1. Images of scales: (a) merino and (b) mohair fibers.

Model Development

We have developed an intelligent fiber classificationsystem, whose structure is shown in Figure 2. There are

IMAGE CA!7ruRE. PROCESSING. AND NORMALIZATION

Cast images of prepared samples were captured with aSony CCD camera mounted on an Olympus optical mi-croscope with a magnification of 4OOx. Digitization wasaccomplished with a video capture card. Image resolu-tion was 800 by 600 pixels with a depth of 8 bits (256gray levels). We randomly took images of scale patternsfrom arbitrary locations on fibers along the fiber lengthand in an arbitrary direction. No visual reference to thefibers influenced the order of images in the database.

We used image processing for feature extraction in thefirst model and normalized images in the second modelbefore they were trained or tested with the artificialneural networks for feature extraction.

Sampling(Cast Image)

Image Capture

Image Processing and/orNormalization

Feature Extraction(Image processing or ANN). t'

Classification

(ANN)

Image ProcessingWe used the following image processing techniques:1. Filtering: A high-pass filter with a kernel of 9 X 9

pixels was applied to the input images to enhance scaleedges and grodually eliminate changing global effectssuch as light variations from row optical images.

2. Contrast stretching: After filtering, images had avery low contrast and the bands occupied in their histo-grams were very narrow. We used a histogram equaliza-tion technique to stretch the ima!!es' nixel v:Jhl~~ to "

~

"-- -- ./

FIGURE 2. flow chart of intelligent fiber classification system.

596 TEX'nLE RESEARCH JOURNAL

(a)

wider range and make scale edges more visible to allo-cate more gray levels where there were more pixels andfewer levels where there were fewer pixels.

3. Thresholding: We employed a single-level thresh-olding operation to produce a binary image based on anadaptive thresholding value and the values of imagepixels and to segment input-scale images into the back-ground, edges of scales, and fiber bodies. The threshold-ing level was varied to suit variations in local or neigh-borhood input image levels.

4. Interactive operations: We also used some interac-tive operations, such as the interest region and assign-ment of some constants.

5. Rotating: Each fiber portion located was automati-cally rotated to a certain direction, e.g., vertical.

6. Morphological operations: We used some basicmorphological operations such as erosion and dilation toeliminate unwanted noise pixels and to fill small holes inthe scale edges' outlines before performing feature ex-traction of scales. Finally, we applied a skeletonizationoperation iteratively to thin the image to a skeleton of asingle pixel.

After these operations, fiber edges and scale edges areclearly visible (Figure 3a).

(b)

Image No171lalization

FIGURE 3. Feature extraction with image processing: (a) processedimage of a merino fiber. (b and c) definition of FI to F9.

FEATURE EXTRACTION

Feature Extraction with Image Processing

With image-processing technology. we extracted fea-ture vectors with nine independent meaningful attributesfor each scale of the fiber population from imagingprocessing. which formed a feature vector instance. Thenine attributes are shown in Figure 3 [12]: Fl = anglebetween the major axis of the best fitted ellipse and thefiber major axis, F2/F3 = lengths of major and minor

The successful implementation of neural networksinto feature classification depends on several techniques,including input data normalization (or pre-processing),feature extraction, and training [10]. After capturing thegray-scale images, they need to be normalized or pre-processed in order for feature extraction to be effective.

To obtain normalized images of 48 by 18 pixels, weused the following steps:

I. Slant normalization: We used three major steps inthe automated alignment process to align a fiber imageby rotating its major axis to the vertical line (with eithertip up or root up): filter/binarize the image, detect theportion of the fiber body, and determine the alignment,i.e., decide the angle of rotation using the Hough trans-form [7].

2. Size normalization: We scaled fiber images to 18pixels in diameter with a locked aspect ratio in the radialand longitudinal directions. The resulting image wascropped or clipped to 48 X 18 pixels. Such windowswere presented as 864 dimensional vectors of 256 gray-level values.

3. Brightness normalization: To avoid the use of first-order statistics for discrimination, we adjusted images tothe same mean brightness and variance with histogram

equalization [81.

597JULY 2002

axes of the best fitted ellipse, respectively, F4/F5 = maxi-

mum and minimum radial distances squared from the grav-ity center in each scale cell, respectively, F6 = area of ascale cell, F7 = total length of the perimeter around a scaleedge, and F8/F9 = differences along the major axis of the

fiber and in its perpendicular direction in each scale, respec-tively. Some of these features are not apparent to humanobservers, while other features can be perceived but theirexact quantitative measurements for separating two classescannot be made by the human visual system.

Feature Extraction(Unsupervised ANN)

&~~~~~~~i~48X18 output units(Reconstructed images)

M hidden units(Features extracted)

FIGURE 4. Feature extraction with neural networks.

It is necessary to monitor how well these extracted fea-tures represent original images. To reconstl1lct an originalimage, the outputs of hidden layer nodes are transferred tothe output layer of the unsupervised segment by multiplyingthem with the transpose of the weight matrix between theinput and hidden layer of the unsupervised network. Animage of 48 X 18 pixels is reconstructed, which shows howmuch of the original information in the input has beencaptured with M features. The closer to the original imagethe reconstl1lcted image is, the more information about thefeatures has been extracted

We set the learning rate at 0.005 (step size), whichlinearly decays to 0.0005 within the first 100 epochs. Thenumber of processing elements in the input layer is set to864 to receive the input images, which equals the numberof elements in the input vectors (48 X 18 pixels) for eachexemplar in the database. We used three different num-bers (M) of units in the hidden layer-eighty, fifty, andtwenty. The number of processing elements in the hiddenlayer in this unsupervised neural network determines thenumber of input processing elements in the input layer ofthe supervised neural network. The number of processingelements in the output layer of the unsupervised neuralnetwork is also set to 864, the same as that used in theinput layer to reconstruct the images.

Feature Extraction with Artificial Neural Networks

Neural network classifiers generalize better when theyhave a small number of independent inputs [4]. It isdesirable to reduce the dimensionality d of a high di-mensional input pattern to a lower dimensional subspaceM (M < d) to extract the intrinsic information beforepresenting it to the classifier network. The goal of thisprocedure is to transfer input data into as few bits aspossible while maximally preserving the source informa-tion in the input data. This means that as much informa-tion as possible from the source must be squeezed intoeach bit. Thus, data compression can be modeled as aprojection operation or feature extraction, where the goalis to find a set of bases that produce a large concentrationof signal power in only a few components. Principalcomponent analysis (PCA), which is an optimal linearfeature extractor, is such a technique [4]. From the inputspace, PCA finds an orthogonal set of M directions wherethe input data have the largest energy, and it extracts Mprojections from these directions in an ordered fashion.The orthogonal directions are called the eigenvectors ofthe correlation matrix of the input vector, and the pro-jections are the corresponding eigenvalues.

PCA can be easily implemented using unsupervisedneural networks through Hebbian learning [5]. An unsu-pervised neural network is particularly well suited forfeature extraction because the most important features ofa given pattern are normally unknown in compressionproblems [3]. Unsupervised learning networks use theidea of correlation between projected data (to a subspaceM) and original input data to process signals. In terms ofnetwork construction, this sort of network normally con-sists of a single hidden layer of neurons or processing

e!ements (PE).The number M of the hidden units in the network

determines the size of subspace, that is, the number ofprincipal components or features used to represent theinput images and train the classifier neural network. Theoutputs of the hidden layer are the features obtained byprojecting the input images onto the subspace of Mdimensions (Figure 4). These features will be used asinputs in the supervised network for classification.

FIBER CLASSIFICATION

We used a multilayer perception (MLP) for fiber clas-sification. It has one hidden layer and uses a tanh (hy-

48X18 input units(Cast images)

TEXTILE RESEARCH JOURNAL598

Results and Discussion

The two models described above use features ex-tracted by different methods to classify fibers. Model oneuses explicit features, which are defined in Figure 3 andcan be clearly identified. However, the features extractedby ANN and used in model two are implicit ones andcannot be physically identified. It is the unsupervisedartificial neural network that determines what the repre-sentative features are in the given images.

perbolic tangent) activation function in the processingelements of the hidden layer and output layer. We alsoapplied a bias activation function to the processing ele-ments in the output layer.

The features extracted with image processing and un-supervised artificial neural networks are used as inputs tothe input layer, while the fiber classes are the outputs inthe output layer. In model one, an input file composed ofnine scale feature vectors extracted by image processingand its corresponding desired output file are assigned. Intotal, feature vectors for 280 scales of merino fibers and280 scales of mohair fibers are collected in this work.When a sample feature vector is fed into the network, thecorresponding desired response of each unit in the outputlayer is encoded to 0 or I, depending on the scale's classmembership. For desired output one, aU merino fibers arespecified as one while aU mohair fibers are specified aszero. For the desired output two, aU mohair fibers arespecified as one and aU merino fibers as zero. Thus, thedesired output vector for merino is encoded as [I, O]Tand the desired output vector for mohair as [0, I]T. If thevalue of output one produced in the output layer of theANN is close to one, the input scale is judged as merino,but if the value is close to zero, the input scale is mohair.

Similar to model one, the features extracted by artifi-cial neural networks in model two are fed into the su-pervised ANN classifier (Figure 5). The numbers of thefeatures are eighty, fifty, and twenty, respectively for thethree different training data sets used in this model,described previously.

CLASSIFICAll0N WITH MODEL ONE

The classification perfonnance of model one is shownin Figure 6 by analyzing the distribution curves of outputone over the fiber class indicator range of (-0.3, 1.3) on

0-- Mohair 0- - Merino

50

40

~ 30>-()~GI

5- 20GIat

10 t-f -_~~::+~l~..:;.~~~~~j--DJ' - , 0 , '

--0 ,0 - - ' '.

-.-, 0.35 0.5 0.65 0.95 1.25

Fibre class indicator

(a) Training data set

~ Mohair

o~-0.25 QO5

Classification(Supervised ANN)

a Merina50

40

-0~ 30>-u~Q)~go 20Q)It

10

~~_~::~_-D-0.35 0.5 0.65

Fibre class Indicator

(b) Tcst data set

o~~-0.25 0.05 0.95 .25

FIC;URli 6. Predication of :II1ificial neural network cla..sifier on tr:lin-ing and test data sets (model I). Fiber indicators: 0 = mohair. I= merino.FIGURE 5. Animal fiber cla..,sification using neural networks.

JULY 2002 599

Merino Mohair Merino Mohair

(a) Input image

(b) M=80

II '~ #

(c) M=50

(d) M=20

Training Test

both training and test data sets. We find a very similartrend in classification for both training and testing. If thefiber class indicator is closer to 0, the scale is more likelyto be mohair. When the fiber indicator is closer to I, thescale will be identified as a merino scale.

If 0.5 is set as the decision boundary or the interceptpoint, only a very small percentage of the merino andmohair scales is wrongly classified during training (4%)and testing (5.4%), since the tails of distribution curvesslightly exceed the boundary. In both training and test-ing, spikes close to one and zero are visible on thedecision distribution curves for merino or mohair scales,respectively, indicating the decisions made are quitedistinct. We also find that the fiber class indicator of avery small number of mohair scales is predicted up tomore than 0.9 in the training data set, and these scales arewrongly classified as merino scales. This indicates thatthese mohair scales possess the characteristics of a typ-ical merino scale.

When a more strict decision rule for fiber classificationis set, the model still presents a very high level ofclassification accuracy. For example, if only a fiber classindicator between 0 :!: 0.1 or I :!: 0.1 is accepted as eithera mohair or merino scale, respectively, more than 70% ofthe scales can be accurately identified either as mohair ormerino scales (Figure 6). In addition, because the clas-sification process of animal fibers is not from individualscales but from fibers, its performance as a fiber classifiercan be significantly improved by considering the assem-bled information of all scales in a fiber section r 121.

FIGURE 7. Input images and their reconstructed images withdifferent number of features.

CLASSIFICATION WITH MODEL Two

The compression operation in model two plays a veryimportant role because it determines the features that theclassifier network learns and finally affects the accuracyof identification and classification. To see how well thefeatures extracted by the unsupervised compression net-work are representative of the original input images, wecompare the reconstructed images with their correspond-ing input images in the training and testing data sets(Figure 7).

In Figure 7, the first row shows some exemplars ofinput images from both merino and mohair fibers in thetraining and test data sets; the second, third, and fourthrows show corresponding reconstructed images ofeighty, fifty, and twenty features, respectively. In theinput images shown in the first row, we observe muchnoise, which may come from the lighting source, themounting medium, or a defective sampling technique.There is less noise in the input images of merino fibers,and their scale margins are more visible than those ofmohair fibers. Consequently, as shown in the remaining

rows, all reconstructed images of merino fibers are muchclearer and closer to the corresponding input images thanthose of mohair fibers. This indicates that the quality ofreconstructed images improves with the quality of inputimages in the input layer.

When three different features (M) are used, the unsu-pervised network learns the important input patternseven from twenty features, especially when the inputimages are of good quality. The quality of the recon-structed images becomes better with more features. Forexample, the images in the second row, which are recon-structed by the unsupervised network with eighty fea-tures, are subjectively distinct (Figure 7). The recon-structed images of fifty features are still close to the inputimages, but not as clear as those of eighty features. Whenthe number of features extracted is reduced to twenty, thereconstructed images become more significantly blurredcompared to those of eighty or fifty features. However,some of the reconstructed images can still be recognized,such as those of merino fibers. Therefore, the unsuper-vised network is able to extract the infonnation con-tained in the input images.

The unsupervised PCA network is able to reduce thedimension of input images to a subspace with a muchlower dimension and extract sufficient features to repre-sent not only the input images in the training data set, butalso the test images not seen by the network before. Thehigher quality of input images can be achieved by usingscanning electron microscopy (SEM) and appropriate

600 TExnLE RESEARCH JOURNAL

coating techniques, but that is beyond the scope of thiswork, which is to develop a low cost animal fiber clas-sification technique.

Although the accuracy of the classification with morefeatures is higher during training, it cannot guarantee theachievement of a generalized model (Figure 8). Whenthe classification rate for fifty and eighty features in thetraining set is higher than that for twenty, it is generallylower with the test data set. This means that with twentyfeatures, the major characteristics of both the merino andmohair fibers have been extracted and used for classifi-cation. Although using more features improves the ac-curacy of classifying fibers during training, the modelneeds to meet more strict criteria to accurately classify afiber during testing. This leads to deterioration in theclassification rate during testing with more features.

develop two models whose main difference is how thefeatures within the images of animal fibers are exb"acted. Inmodel one, we employ an image processing technique toextract nine explicit features, while we use unsupervisedartificial neural networks in the second model to extracteighty, fifty, and twenty implicit features.

We employ supervised artificial neural networks to clas-sify these fibers, based on the features extracted with imageprocessing and unsupervised artificial neural networks. Wefind that classification with features explicitly extracted byimage processing is more accurate than with features fromunsupervised artificial neural networks. However, classifi-cation with combined unsupervised and supervised artificialneural networks is more robust. since it needs only verylimited image processing and prior knowledge. The classi-fication accuracy of model two will be improved by devel-oping more powerful learning strategies.

100 :;~90 t:::::::::::::::

~ merino80

--- mohairl 70Gm

~ 600

~

~ 100"inIII

Eo 90U

Training

OOf:::::::

~ ~;~60

20 50

Features (M)

80

FIGURE 8. Fiber classification accuracy of ANN with different fea-tures.

We observed that the classifying accuracy is higher formohair fibers in both training and testing, which meansthat mohair fibers have more common characteristics(Figure 8). This coincides with the observation in modelone using integrated optical and image processing andartificial neural networks (Figure 6).

Conclusions

We have developed an intelligent system to objectively

classify two types of animal fibers. merino and mohair.

using image processing and artificial neural networks. We

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Manuscript rl'cl'ivl'd Dl'cl'I1,bt'r 7. 2INN): accl'ptl'd Octohl'r 26. 2()()/.