Modified Convolutional Neural Network Architecture for Batik Motif Image Classification

Ardian Yusuf Wicaksono, Nanik Suciati, Chastine Fatichah, Keiichi Uchimura, Gou Koutaki


Batik is one of the cultural heritages of Indonesia that have many different motifs in each region as well as in its usage. However, the Indonesians sometimes not knowing the batik motif that they’re wearing every day, and sometimes they have a batik image without knowing batik information contained in their batik image. With the growing number of images of batik and batik motifs, a classification method that can classify various motifs of batik is required to automatically detect the motif from the batik image. Image processing using the Deep Learning especially for image classification is widely used recently because it has good results. The most popular method in deep learning is Convolutional Neural Network (CNN) which has been proved robust in natural images. This study offers a batik motif image classification system using CNN method with new network architecture developed by combining GoogLeNet and Residual Networks named IncRes. IncRes merges the Inception Module with Residual Network structure. With the 70.84% accuracy, the system can be used to classify the batik image motif accurately.


Batik, Classification, Convolutional Neural Network, Deep Learning, Machine Learning, Pattern Recognition.

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