Indonesian Batik Image Classification Using Statistical Texture Feature Extraction Gray Level Co-occurrence Matrix (GLCM) and Learning Vector Quantization (LVQ)

Nafik’ah Yunari, Eko Mulyanto Yuniarno, Mauridhi Hery Purnomo

Abstract


Batik, as a cultural heritage from Indonesia, has two kinds of true batik, batik tulis or handwritten batik and batik cap or stamped batik. However, it is still difficult to discern what is really batik and which are not. So many clothes are claimed as batik whereas they aren’t. These pseudo batiks have spread on the market and become deceitful. The objective of this research is to obtain the pattern value of batik to recognize the real batik using feature texture extraction with Gray Level Cooccurrence Matrix (GLCM) as a method for extracting textural features and Artificial Neural Network Learning Vector Quantization (LVQ) as a method to classify. The result of this study shows the average accuracy of data training without normalization of 90.43% and data with normalization of 98.40%. While on data testing the average value of accuracy on the dataset without normalization of 92.79% and after normalization the average value is 98.98%, so the increase in the average value of accuracy of 8%.

Keywords


Batik; GLCM; Image; Indonesian Batik; LVQ; Texture Feature;

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References


UNESCO, Indonesian Batik: Inscribed in 20019 on the Representative List of Intagible Cultural Heritage of Humanity, United Nations. http://www.unesco.org/culture/ich/en/RL/indonesian-batik-00170.

A. E. Munarko, Y. Bimantoro, A. F. Kurniawardhani, and N. Suciati, “Batik image retrieval based on enhanced micro-structure descriptor,” in Computer Aided System Engineering (APCASE), 2014 Asia-Pacific Conference, 2014, pp. 65-70.

I. Nurhaida, R. Manurung, and A.M. Arymurthy, “Performance Comparison analysis Feature Extraction Methods for Batik Recognition,” in Proc. Int. on Advanced Computer Science and Information System (ICACSIS), 2012, pp.207 - 212.

T. Handhayani, "Batik Lasem Images Classification Using Voting Feature Intervals 5 and Statistical Features Selection Approach". International Seminar on Intelligent Technology and Its Application (ISITIA), 2016

A. H. Rangkuti, R. B. Bahaweres and A. Harjoko, (2012, December). “Batik image retrieval based on similarity of shape and texture characteristics,” in Advanced Computer Science and Information Systems (ICACSIS), 2012 International Conference, 2012, pp. 267- 273.

T. Kohonen, R. Schroeder, S. Huang, “Self-Organizing Maps”, Springer Series in Information Sciences, Volume 3, Berlin Heidelberg Edition: 30, 2001.

R. Pawening, R. Dijaya, T. Brian, and N. Suciati, “Classification of Textile Image using Support Vector Machine with Textural Feature,” in International Conference on Information and Communication Technology and Systems (ICTS), IEEE, 2015, pp. 163-168.

V. S. Moertini and B. Sitohang, “Algorithms of clustering and classifying batik images based on color, contrast and motif,” Journal of Engineering and Technological Sciences, 37(2), 2015, pp. 141-160.

A. E. Minarno, Y. Munarko, A. Kurniawardhani, F. Bimantoro and N. Suciati, “Texture Feature Extraction Using Co-Occurance Matrices of Sub-Band Image For Batik image Classification,” in Proc. 2nd Int. Conf. on Information and Communication Technology (ICoICT), Bandung, 2014, pp.249-254.

M. Tuceryan, A. K. Jain, "Texture Analysis", Handbook of Pattern Recognition and Computer Vision (2nd ed.), World Scientific Publishing Company, 1998, pp. 207-248.

A. Ghosh, M. Biehl, and B. Hammer, “Performance Analysis of LVQ Algorithm: A Statistical Physics Approach,” Journal Neural Networks – 2006 Special Issue, Vol 19, 2006, pp. 817-829.

Z. K. Huang, P. W. Li and L. Y. Hou, “Segmentation of textures using PCA fusion based Gray-Level Co-Occurrence Matrix features,” in Test and Measurement, 2009. ICTM'09. International Conference on, Vol. 1, IEEE, 2009, pp. 103-105.

Standar Nasional Indonesia (SNI), SNI 8184, 2015, Tiruan batik dan paduan tiruan batik dengan batik, pengertian dan istilah. Badan Standardisasi Nasional, Jakarta.

R. Gonzalez and R. Woods, Digital Image Processing. New York: Addison-Wesley Publishing Company,Inc, 1992.

R. M. Haralick, K. Shanmugan and I. Dinstein, “Textural Features for Image Classification,” in IEEE Transactions on Systems: Man, and Cybernetics SMC, Vol. 3, 1973.

R. Remco, F. Eibe, H. Mark, K. Richard, R. Peter, S. Alex, and S. David, WEKA Manual 3-8-1, Hamilton, New Zealand: University of Waikato, 2016.


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ISSN: 2180-1843

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