TOWARDS CLASSIFYING CLASSICAL BATIK IMAGES - Unpar

TOWARDS CLASSIFYING CLASSICAL BATIK IMAGES
Veronica S. Moertini 1
Email: moertini@home.unpar.ac.id
Fax: (022) 2501023
Abstract:
Many kinds of batik style have been produced including the one called classical batik. Currently, batik images have
been produced and used such as by government institutes and online stores. Sometimes people would need to search
certain style of batik electronically. To speed up the search, batik could be first classified so that the search is done
in a limited collection of batiks. The aim of this research is to find a method that could classify classical batik
images automatically based on the shape of ornaments and batik textures. Canny edge detector is used to generate
the edges of the ornaments. Shape feature is generated using Fourier transform, moments and points of edge.
Wavelet-based and discrete-cosine-based techniques are used to generate the texture feature. Image similarity
between the new image and each training image is computed for each feature. Finally, a back-propagation neural
network is used to determine the class of the new image.
Keywords: image classification, shape feature, texture feature
Introduction
Batik has a very special place in the world of textiles. Many kinds of batik style have been
produced, such as batik kraton (batik from the courts), batik sudagaran, batik Belanda, batik
Cina, batik Djawa Hokokai, and batik Indonesia which also called batik modern [(Kerlogue, F.,
2004), (Doellah, S., 2002)]. These types of batik differ in terms of their motifs, the way they are
produced, color variety, coloring material, and cloth material. Traditional batiks from the courts
of Java are also called classical batik (van Roojen,P., 2001). They are still largely being produced
and their motifs are sometimes used in batik modern design. Currently, batik images have been
produced and used such as by government institutes and online stores to provide services to
people who need them. Sometimes people would need to search certain styles of batik. To speed
up the search, batik could be first classified so that the search is done in a limited collection of
batiks. At this early stage, this research aims to find a method that could classify classical batik
images automatically (into its sub-classes) based on their ornaments shapes and textures.
Batik
The items formed classical batik patterns or motifs can be broken down into two parts: the main
and additional ornaments, and isen-isen (small ornaments used to fill the empty space in or
between ornaments). The main ornaments define the motif style and they usually have some
1 Dept. Teknik Informatika ITB (Doctorate Student), Labtek V, Jl. Ganesha 10, Bandung 40132, Indonesia
Jur. Ilmu Komputer (Lecturer), Gedung 9, Unika Parahyangan, Jl. Ciumbuleuit 91, Bandung 40191, Indonesia
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meaning. Some examples of the main ornament are garuda, meru (mountain), trees, temple,
parang. Based on the ornaments and their structures, classical batik can be classified into two
major classes, which are geometry and non-geometry [(Kerlogue, F., 2004), (Doellah, S., 2002),
(Susanto, 1980)]. The two classes can then be further divided into subclasses, which are banji,
ganggong, ceplokan, etc. (See Table 1.)
Tabel 1. The classification of classical batik.
Motif
Geometrical Motifs
Description Examples of Batik
banji Its basis is swastika, a simple cross with arms of equal
length, each arm bent at right angles pointing in the
same direction. The motif is built up from swastika
interconnected at angles of 90 degrees.
banji bengkok, guling, kerton
ganggong Square, filled with ganggong plant and diagonal lines ganggong branto, sari, rejuna,
kurung, yojana
ceplokan Repetitive shapes of circle, ellip, square, square with kawung picis, sen; ceplok keci,
smooth corner, rosette, star, cross sections of fruit. nogosari
anyaman, Recognized by the rows of dots and short stripes that rengganis, nitik krawitan, tirta
nitik run parallel and at right angles in a pattern that
imitates woven decoration.
teja alit
parang, Gently curved design is run diagonally in a powerful parang barong, baris, centong,
lereng rhythm, the space between parallel running is filled
with small ornaments (isen-isen).
Non-Geometrical Motifs
kembang, kurung, rusak, etc
semen Filled with mountains (meru) or places to grow
plants, plants and animals ornaments. Ornaments are
placed almost freely.
semen gurdo, kasut, Yogya
lung-lungan Similar to semen but with less ornaments and without Babon angrem, grageh walu,
meru.
lung klewer, peleman
The Proposed Classification Technique
The aim of the classification is to classify classical batik images into their sub-classes (banji,
ganggong, ceplokan, etc). Hundreds of batik images have been collected, and after observing the
classical batik images, it is found that: (1) The image color could be light (high intensity) or dark
(low intensity). (2) The edges of batik ornaments could be clearly visualized (with high contrast)
or fuzzy (with low contrast), and the size of the edge lines can be thick (clear) or thin (unclear).
(3) The size of the main batik ornaments could be small (producing fine texturous images),
medium or large (producing coarse texturous images). From the result of experiments in
detecting batik image edges using Canny methods (see section Experiment), it is found that: (1)
The images having batik ornaments of large size and high contrast with their background
produce clear edges and easily recognizable shapes. However, sometimes, the ones of low
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contrast produce unrecognizable shapes. (2) Both images of high and low contrast having small
size of ornaments produce complex edges as they are “contaminated” by isen-isen edges, and the
shapes could not be recognized. (3) Semen and lung-lungan ornaments, most of the time, are
hard to detect as they merge with other crowded ornaments or isen-isen.
Figure 1. Examples of batik motifs.
Considering these facts, it is concluded that classical batik images can not be classified using the
shapes or the textures alone. Therefore, the classification system utilizing ornaments shape and
image textures is proposed (see Figure 2). During off-line stage, first, the training images are
preprocessed. Secondly, texture and color features are extracted from the images and stored. On
on-line mode, the new image being classified is preprocessed, and the features are generated.
The features are then compared to each pair of features stored id database, which generated a set
of similarity values between the new image and the training images. Using the similarity values,
backpropagation neural networks and k-nearest neighbor classifier then determines the class of
the new image.
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training
images
off-line
online
new
image
image
preprocessing
image
preprocessing
texture and
shape features
generation
texture and
shape features
generation
Figure 2. A schematic of classical batik images classification system.
Images Preprocessing. The pre-processing steps are: cropping the images to find the part of the
images that are meaningful (in the experiment, the cropped size is 128 x 128 pixels), converting
them to gray-scale images.
Texture Features Generation. Based on experiments, (Randen, Husoy 1999) concludes that there
is no superior method for texture classification. Some methods may be good for certain images,
but inappropriate for other images, with respect to the classification error. In this study, two
methods would be implemented, which are discrete wavelet transform (DWT) and discrete
cosine transformed (DCT) based. For each method, two approaches would be implemented:
content-based and region-content-based. Content-based steps: (1) 2-level DWT (or DCT) is
applied to an image. (2) Statistical measures (mean and variance) are computed from the
coefficient resulted from Step 1 and stored as the image feature. Region-content-based steps: (1)
Each image is segmented into windows (which could be overlapping). (2) 2-level DWT (or
DCT) is applied to each window and the coefficients are normalized. (3) The result of Step 2 are
clustered using k-Means algorithm. (4) Mean and variance of each cluster are computed and
stored as the feature. These methods are the modification version of the techniques employed in
(Wang, et.all, 1997), (Bartolini., 2001), (Feng, Jiang, 2002) and (Singh, 2003). As most images
are stored as compressed form of JPEG format, the advantage of using DCT is that the features
can be directly generated from the compressed form (without decompressing the images first),
which reduces the computation time.
Shape Feature Generation. As shown on Figure 1, batik motifs, which are used to determine the
batik class, are “hidden” among isen-isen. Therefore, making sure that motif shapes could be
obtained is important. Here, the intensity of images is adjusted so that the motif shapes are clear.
Next, to generate shape features, three methods are proposed: enhanced Fourier descriptor
(Zhang, Lu, 2002), moment invariants (Lu, 1999) and elastic template matching (Hirata, Kato,
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image features
and classes
classification
image
class

1992). Fourier descriptor methods: (1) The image is presented to Canny edge detector. (2) The
centroid of the edge points is computed. (3) Compute the distance of each sample edge point to
the centroid. (4) Apply Discrete Fourier Transform to the result of step 3. (5) Normalize the
coefficients of step 4 by dividing each coefficient (Fi) with the first coefficient (F0) and store
them as the features. Moments invariant method: Three order moments are computed from the
preprocessed images and the result are stored as the feature. Elastic template matching methods:
(1) The image is presented to Canny edge detector. (2) The output of step 1 (an array containing
1s and 0s) is stored as the feature.
Classification using k-Nearest Neighbor. The basic idea of the classification process is adopted
from (Sheikholestami et.all, 1998) with some modification. The classification process consists of
four main steps: (1) Compute the new image shape and texture features and the similarity values
between the new image and all training images using shape and texture features. Find two sets of
training images that are most similar to the new image in terms of the ornaments shape and the
image texture. (2) Combine the two sets resulting in step 1. Let Rq be the new set. (3) Compute
the overall image similarities of images in Rq using multi-layer back-propagation neural network.
(4) Sort the new similarity values obtained from step (3), and using k-nearest neighbor
classification technique, determine the class of the new image.
Computing image similarities. Texture feature. For content-based, the distance between 2
images is computed using Euclidean distance. Then the distance is transformed into similarity
value as follows:
SimValue(i,j) = 1 – distance(i,j) / max of Distance (1)
where i,j denoting the index of the two images being compared and Distance is a set of all
distance between images. For region-content-based, the method in (Bartolini, 2001) is applied.
Shape feature. The main steps are: (1) The new image is segmented into windows and features
are generated from each window. (2) The feature of a training image is compared to the feature
of each new image windows. (3) For moments invariant and Fourier descriptor, the similarity
value is computed using Eq. 1. For elastic template matching: similarity is computed by
determining the correlation between the two features. The largest value among all similarity
values of the windows is selected as the similarity value between the new image and training
image.
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Training the back-propagation neural networks. To train the neural network (see Figure 3) and
compute the proper weights, a set of images that are visually similar (positive examples) and a
set of images that are not similar (negative examples) are provided. The input for the training is
the similarity values of the images computed from texture and shape feature matching.
x1
x2
input
layer
V21
V11
V22
V12
hidden
layer
Figure 3. Back-propagation neural networks.
Experiment
At this early stage of research, experiments have been conducted to find the proper technique of
obtaining image edges and to prove visually that shapes and textures should be used in
classifying images.
By observing a sample of batik images, it is found that many of them have unsharp motif edges
but sharp edges of isen-isen or other additional ornaments that have no meaning. Applying
Canny edge detector to such images directly would generate edges that are inappropriate for
classifying. Figure 4 shows banji batik edges where swastika shape, which is needed for
classifying is hidden among other unnecessary ornaments.
Figure 4. Banji batik edges shape without image enhacement.
To deal with this problem, images are enhanced. First, the intensity of the shapes having low
value of intensity is increased. In this experiment, intensity value of [0 0.5] is transformed to [0.5
1]. Second, the image is sharpened by stretching intensity value to a certain range, in this
experiment, it is [0, 1]. Canny detector is then applied to the enhanced images. Figure 5 shows
some edges generated from images shown on Figure 1. It is shown on Figure 5.a, that swastika
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y12
y22
output
layer
o

appears clearly. By enhancing the images and using a certain threshold value (in this experiment,
it is 0.5) to Canny detector, isen-isen is sometime eliminated from the edges giving better
shapes.
a.banji b. ceplok c. lung-lungan d. semen
Figure 5. Batik edges detected by Canny detector.
It also is found that the output of Canny detector applied to batik images having fine motif is fine
and dense edges (see Fig. 6). Applying the methods mentioned previously to generate shape
features from these edges would potentially give bad or erroneous features. Generating texture
feature from this kind of image is more appropriate.
Figure 6. An example of Canny output having fine and dense edges.
Conclusion
Image preprocessing of classical batik images plays an important role in generating the
appropriate features that could lead to high accuracy result of the classification. As there are
many batik images containing large and small shapes of ornaments, generating feature for both,
in the form of shape and texture feature, would be needed by the classification technique in order
to increase the accuracy. Further work is needed to prove that the classification technique
proposed performs well. The work would include implementing the system and conduct
experiments, with the aim of observing the performance of the technique, using large number of
images representing real world batik images.
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