Preliminary Final Project Assignment

CULTURE DEPENDENT BATIK CLASSIFICATION

WITH ANALYTICAL FUNCTION FOR FEATURE

EXTRACTION

AYUNINDA DWI NUGROWATI NRP. 7410040044

D4 STUDY PROGRAM OF INFORMATICS ENGINEERING

INFORMATION AND COMPUTER ENGINEERING

ELECTRONIC ENGINEERING POLYTECHNIC INSTITUTE OF

SURABAYA

2013

A. TITLE

Culture-dependent Batik Classification with Analytical Function for Feature

Extraction.

B. INTRODUCTION

Indonesia is a country rich of culture, one of them is batik. Batik is a culture

from Indonesia which had been appointed by UNESCO as Masterpieces of the Oral

and Intangible Heritage of Humanity. Batik is an art that has highly value in Indo-

nesia. In the past time, batik is a specific job that can be only done by women. It is

called as a specific job for women because batik production is done using canting as

their tool to draw its pattern in batik and it can only be done by women. Until they

develop batik cap that make man can produce it too.

In the current time, batik is highly favored by Indonesian people. Pattern and

color of batik actually is affected by foreign influence. At first, pattern and color ba-

tik is limited. But after foreign culture come to Indonesia, this patterns and colors

increasing. For example a bright color like red is introduced by Tionghoa, and flow-

ers and objects that brought by colonist like horse-drawn carried patterns is intro-

duced by European. Pattern and colors of batik actually has specified meaning and

even some of the pattern only can be used by certain circles. The example is parang

pattern which only can be used by nobles. There is even batiks with specify pattern

that used to culture ceremonies. The variety of pattern and color in batik often affect-

ed by its region.

C. PROBLEM

Batik is an inheritance from ancestor that always keeps from generation to

generation. Batiks pattern that highly diverse is caused by have specific meaning in

each pattern that inherited. Since their pattern is highly diverse, batik is very hard to

be classified. Each region has their own specify pattern that reflect their identity from

their own region, but it is not rare to have pattern that almost same from one region

to another. Its not only pattern that make it different, but also from its color that has

specific meaning too. The more variety of batik in Indonesia increases difficulty to

classify these batiks.

D. PROBLEM SCOPE

For the scope of the problem, this application will use batik from east java for

the dataset. This dataset will be captured from image batik from east java and will be

placed on smartphone android that will be used for this research.

E. OBJECTIVE

This research proposes a new system for Batik classification with presenting

culture-dependent Indonesia Batik from several traditional Batik origin places. It

provides an analytical function for feature extraction by involving color and shape

features. 3D-Vector Quantization is applied for color feature extraction. The system

uses Hue-moments to extract shape features. This research presents the system in

mobile application and makes the online classification from camera-capture image.

F. CONTRIBUTION

This research has an advantage for peoples who have interest in batik. This

application makes people who love batik can learn about the origin place of batik

pattern. It uses a mobile phone so that can be easy to carry and use. This application

will provide a place of the origin batik from photos taken by the camera on the mo-

bile phone.

G. DESIGN SYSTEM

This application will divide image into two categories, image database and

image query. Where image database is data image that store in database application

and image query is a new image that will be compared with image database. Figure

of the system which will be made shown below.

Figure 1. Design System

For this research, we use three methods; they are 3D-Color Vector Quantiza-

tion, Hu Moment, and Color Moment. 3D Color Vector Quantization (CVQ) is used

to calculate the weights of the colors in the images of batik. Hus Moment is used to

calculate the weight of its shape or motif. And Color moment is used to compare the

Madura Traditional

Batik

Traditional Batik Batik Design

Mobile Phone (Image Capture)

Batik Dataset Batik Image Query

Color Feature

Hu Mo-ment

Shape Feature

Feature Extraction

Weighting Mechanism

Color Moment

Similarity Measurement Batik Origin Place

Jogja Traditional

Batik

Hu Moment

weight between the images of batik in the database with the query image. To learn

more about all three methods, it will be described below.

a. 3D Color Vector Quantization (CVQ)

For this method, noise removal and 4x4 image partitioning are applied

before extracting color feature. Then, for each block it extract color infor-

mation using the histogram from 3D-Color Vector Quantization of RGB color

space. It use the 64x64x64 quantization size of RGB color space so that it can

be represented with 125 positions in the RGB color space, as shown in Figure

2.

Figure 2. Illustration of 3D-Color Vectore Quantization of RGB color

space

The metadata of color feature MCLb for block b can be described as fol-

lows:

Where:

fci is a color feature of i-th color histogram from 3D-Color Vector Quan-

tization of RGB vector space.

Figure 3 illustrates the mechanism of color feature extraction: image par-

titioning, Color Vector Quantization, and color histogram.

Figure 3. Mechanism of color feature extraction

After extracting the color feature, the metadata of feature is created. This

process is done offline and saved to metadata repository. Because it apply 4x4

image partitioning for each image the metadata of each feature consists of 16

blocks.

They referred to basic concept of Mathematical Model of Meaning

(MMM) for creation of metadata space. The information on data items is given

in the form of matrix. Each data item is provided as fragmentary metadata

which is independently represented one another. The information of each data

item is represented by its features. The n basic data items are given in the form

of an n by m matrix M. for given n basic data items, each data item is charac-

terized by m features. By using this matrix M, the orthogonal space is comput-

ed as the metadata space. Metadata items which are represented in m-

dimensional vectors are mapped into the orthogonal metadata space.

The color metadata (MCL) is shown in below. The attributes consisted of

color metadata from histogram of 3d-Color Vector Quantization of the RGB

vector space[1].

b. Hus Moment

The non-orthogonal centralized moments are translation invariant and

can be normalized with respect to changes in scale. However, to enable invari-

ance to rotation they require reformulation. Hu described two different meth-

ods for producing rotation invariant moments. The first used a method called

principal axes, however it was noted that this method can break down when

images do not have unique principal axes. Such images are described as being

rotationally symmetric. The second method Hu described is the method of ab-

solute moment invariants and is discussed here. Hu derived these expressions

from algebraic invariants applied to the moment generating function under a

rotation transformation. They consist of groups of nonlinear centralized mo-

ment expressions. The result is a set of absolute orthogonal (i.e. rotation) mo-

ment invariants, which can be used for scale, position, and rotation invariant

pattern identification. These were used in a simple pattern recognition experi-

ment to successfully identify various typed characters. They are computed

from normalized centralized moments up to order three and are shown below.

Hu invariant moment:

Finally a skew invariant, to help distinguish mirror images, is:

c. Color Moment

This mechanism analyses the distribution of color information to deter-

mine representative features. First, we transform the color space of images into

hybrid color spaces with combining HSL and CIELAB color spaces. The im-

age segmentation is then applied in our image search system using our Pillar-

K-means algorithm.

The system extracts color moments of an image, and calculates the color

distances for the color weight, the texture density for the structure weight and

the shape property for the shape weight. The color moments have been suc-

cessfully used in many retrieval systems and proved to be efficient and effec-

tive in representing color distributions of images. The color moments gives

three kinds of orders, which are the first order (mean ), the second order (var-

iance ) and the third order (skewness s).

Where fij is the value of the i-th color component of the image pixel j,

and N is the number of pixels in the image.

To obtain the color weight, the color distances are calculated from the

first order color moment by applying the shape independent clustering [21] in

order to construct and calculate distances of color hierarchy. For measuring the

structure weight, the texture density is calculated from the second order of col-

or moment to be more sensitive to scene the structures of images. The seg-

mented images from the Pillar-Kmeans algorithm are transformed into gray-

scale images to reduce the variance of the second order. To calculate the shape

weight, the shape property is obtained from the third order of color moments.

In this case, the images are converted into binary images in order to sharpen

the skewness. The edges detection is then applied before calculating the third

order of the color moments for shape property. The edges detection is then ap-

plied before calculating the third order of the color moments for shape proper-

ty. The design of the proposed automatic weighting mechanism for our image

search is shown in Figure 6. For the normalization, we set more weighted

consideration for the color weight because the color feature is essential and

dominance to determine the structure and shape weights. In the case of our im-

age search, the color feature is weighted twice rather than the structure and

shape features.

Figure 4. Design of the proposed automatic weighting

mechanism our image search system

H. RELATED WORKS

Batik Indonesia is very loved by the people because of its style and color varie-

ty. Due to a very diverse motif, batik is very difficult to classify. Some researchers

have tried to classify this batik in various ways. One of them is the research from Al-

varez A. Primary, Nanik Suciati, Diana Purwitasari Department of Informatics, Fac-

ulty of Information Technology, Institute of Technology are trying to classify batik

by using Fuzzy C-Means with texture features. In this research, they used the Dis-

crete Wavelet Transform (DWT), Rotated Wavelet Filters (RWF), and Grey Level

Co-occurrence Matrix (GLCM) as the features of images to identify the texture of

batik. Then, the extraction will be grouped into the patterns by using Fuzzy C-Means

clustering (FCM). FCM used directly to label the batik that has more than one pat-

tern gives fairly accurate results [4]. Unfortunately in this research Fuzzy C Means

Clustering is not very suitable for use in clustering data that too spread out like this

batik motif.

Besides that research, there are other researches of classifying batik, one of

them is the research of Dhani Pratikaningtyas, Imam Santoso, Ajub Ajulian Z. on

Batik Classification Method Using Wavelet Transformation Pack. The researchers

use several different types of wavelet to classify batik texture. The types of wavelet

used are Daubechies-2, Daubechies-3, and Coiflet-1. This classification begins with

decomposition process to obtain the wavelet coefficients that will be used to calcu-

late the value of energy and entropy of each image and then combined in the data-

base. The next process is to compare the energy and the entropy between images that

will be classified by the image in the database. The last step is to find the Euclidean

distance to show that image in one of the tests is included in the database Class [5].

But this study only use the data from batik texture alone, do not use the color data of

batik.

In another research using the Wavelet Transform as a method for extracting

features batik and Neural Network as a method for classify batik feature said that the

highest accuracy of 100% for the testing data is the same as the training data and

78.26% is achieved for testing data is the air-different from the training data. Both

accuracy obtained on the value of learning rate 0.8, using the momentum of 0.9, the

number of hidden layer nodes composition [40 10 1] on the 5th level decomposition.

These results are explained by Bernardine Arisandi, Nanik Suciati, and Arya Wijaya

Yudhi in a paper entitled Pengenalan Motif Batik dengan Rotated Wavelet Filter

dan Neural Network [6]. However, as in previous papers, the research is done by

taking shape features of batik, does not include other features such as color features.

I. WORK SCHEDULE

No. Detail Month

A. PREPARATION I II III IV V VI

1 Establishment of Work Plan

3 Data Gathering

4 Tools Preparation

B. IMPLEMENTATION

1. Design Flowchart

2. Design Flow of Program

3. Design Flow of Database

4. Design Interface (GUI)

5. Making Application

6. Testing Application

7. Bug and Error Correction

C. MAKING REPORT

1. Data Analysis

2. Writing Draft Report

3. Revision

J. COST ESTIMATE

Cost of Tools

No Tools Name Use for Cost

1 Smartphone Android Device used for research Rp. 4.000.000

2 Camera Device used for capturing ba-

tik image for dataset

Rp. 4.000.000

3 Paper 1 rim Print paper Rp. 60.000

4 Transportation fee Transportation for searching

data batik

Rp 100.000

K. REFERENCES

[1] Barakbah, A., Kiyoki, Y.: 3D-Color Vector Quantization for Image Retrieval

Systems

[2] http://en.wikipedia.org/wiki/Image_moment

[3] Barakbah, A., Kiyoki, Y.: Image Search System with Automatic Weighting

Mechanism for Selecting Features

[4] Pratama, Alvian A., Suciati, N., Purwitasari, D.: Implementasi Fuzzy CMeans

untuk Pengelompokan Citra Batik Berdasarkan Motif dengan Fitur Tekstur

[5] Pratikaningtyas, D., Santoso, I., Ajulian Z, A.: Klasifikasi Motif Batik

Menggunakan Metode Transformasi Paket Wavelet

[6] Arisandi B., Suciati N., Wijaya A.: Pengenalan Motif Batik dengan Rotated

Wavelet Filter dan Neural Network.