Implementation of deep neural networks (DNN) with batch normalization for batik pattern recognition

• Published in: International journal of electrical and computer engineering (Malacca, Malacca) Vol. 10; no. 2; p. 2045
• Main Authors: Nurhaida, Ida, Ayumi, Vina, Fitrianah, Devi, Zen, Remmy A. M., Noprisson, Handrie, Wei, Hong
• Format: Journal Article
• Language: English
• Published: Yogyakarta IAES Institute of Advanced Engineering and Science 01.04.2020
• Subjects: Accuracy; Algorithms; Artificial neural networks; Classification; Cloth; Complexity; Computing time; Feature extraction; Feature recognition; Model accuracy; Neural networks; Pattern recognition; Reduction; Regularization; Texture recognition; Indonesia
• ISSN: 2088-8708; 2088-8708
• DOI: 10.11591/ijece.v10i2.pp2045-2053

One of the most famous cultural heritages in Indonesia is batik. Batik is a specially made drawing cloth by writing Malam (wax) on the cloth, then processed in a certain way. The diversity of motifs both in Indonesia and the allied countries raises new research topics in the field of information technology, both for conservation, storage, publication and the creation of new batik motifs. In computer science research area, studies about Batik pattern have been done by researchers and some algorithms have been successfully applied in Batik pattern recognition. This study was focused on Batik motif recognition using texture fusion feature which is Gabor, Log-Gabor, and GLCM; and using PCA feature reduction to improve the classification accuracy and reduce the computational time. To improve the accuracy, we proposed a Deep Neural Network model to recognise batik pattern and used batch normalisation as a regularises to generalise the model and to reduce time complexity. From the experiments, the feature extraction, selection, and reduction gave better accuracy than the raw dataset. The feature selection and reduction also reduce time complexity. The DNN+BN significantly improve the accuracy of the classification model from 65.36% to 83.15%. BN as a regularization has successfully made the model more general, hence improve the accuracy of the model. The parameters tuning also improved accuracy from 83.15% to 85.57%.