Texture feature extraction using co-occurrence matrices of sub-band image for batik image classification

Agus Eko Minarno*, Yuda Munarko, Arrie Kurniawardhani, Fitri Bimantoro, Nanik Suciati

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

31 Citations (Scopus)

Abstract

In this study, we propose a new method to extract texture features of batik images. The proposed method is called co-occurrence matrices of sub-band images. This method is proposed to overcome the problem in classifying batik images that are acquired randomly from the internet. The problem of those images is the batik images contain various types of noise, such as unbalanced brightness, there are folds on fabrics images, the different size of basic motifs, low contrast, and there is watermark on the images. This method combines the advantages of gray-level co-occurrence matrices (GLCM) and discrete wavelet transform (DWT). First, the original image is decomposed using DWT to provide sub-band images. Second, GLCM is applied to sub-band images to extract the texture features. Those features will become the input for the probabilistic neural network (PNN). The results show that this method is robust enough to classify batik images. The maximum accuracy that can be achieved is 72%.

Original languageEnglish
Title of host publication2014 2nd International Conference on Information and Communication Technology, ICoICT 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages249-254
Number of pages6
ISBN (Electronic)9781479935819
DOIs
Publication statusPublished - 30 Sept 2014
Event2nd International Conference on Information and Communication Technology, ICoICT 2014 - Bandung, Indonesia
Duration: 28 May 201430 May 2014

Publication series

Name2014 2nd International Conference on Information and Communication Technology, ICoICT 2014

Conference

Conference2nd International Conference on Information and Communication Technology, ICoICT 2014
Country/TerritoryIndonesia
CityBandung
Period28/05/1430/05/14

Keywords

  • batik
  • gray-level co-occurrence matrices
  • probabilistic neural network
  • wavelet

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