TY - GEN
T1 - Texture feature extraction using co-occurrence matrices of sub-band image for batik image classification
AU - Minarno, Agus Eko
AU - Munarko, Yuda
AU - Kurniawardhani, Arrie
AU - Bimantoro, Fitri
AU - Suciati, Nanik
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/9/30
Y1 - 2014/9/30
N2 - 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%.
AB - 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%.
KW - batik
KW - gray-level co-occurrence matrices
KW - probabilistic neural network
KW - wavelet
UR - http://www.scopus.com/inward/record.url?scp=84909957464&partnerID=8YFLogxK
U2 - 10.1109/ICoICT.2014.6914074
DO - 10.1109/ICoICT.2014.6914074
M3 - Conference contribution
AN - SCOPUS:84909957464
T3 - 2014 2nd International Conference on Information and Communication Technology, ICoICT 2014
SP - 249
EP - 254
BT - 2014 2nd International Conference on Information and Communication Technology, ICoICT 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Conference on Information and Communication Technology, ICoICT 2014
Y2 - 28 May 2014 through 30 May 2014
ER -