Comparison of methods for Batik classification using multi texton histogram

Agus Eko Minarno*, Ayu Septya Maulani, Arrie Kurniawardhani, Fitri Bimantoro, Nanik Suciati

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)


Batik is a symbol reflecting Indonesian culture which has been acknowledged by UNESCO since 2009. Batik has various motifs or patterns. Because most regions in Indonesia have their own characteristic of batik motifs, people find difficulties to recognize the variety of Batik. This study attempts to develop a system that can help people to classify Batik motifs using Multi Texton Histogram (MTH) for feature extraction. Meanwhile, k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM) algorithm were employed for classification. The performance of those classifications is then compared to seek the best classification method for Batik classification. The performance is tested 300 images divided into 50 classes. The results show the optimum accuracy achieved using k-NN with k=5 and MTH with 6 textons is 82%; however, SVM and MTH with 6 textons denote 76%. According to the result, MTH as feature extraction, k-NN or SVM as a classifier can be applied on Batik image classification.

Original languageEnglish
Pages (from-to)1358-1366
Number of pages9
JournalTelkomnika (Telecommunication Computing Electronics and Control)
Issue number3
Publication statusPublished - Jun 2018


  • Batik
  • Classification
  • K-nearest neighbor
  • Multi texton histogram
  • Support vector machine


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