Batik Classification using Deep Convolutional Network Transfer Learning

Yohanes Gultom (Universitas Indonesia)
Aniati Murni Arymurthy (Universitas Indonesia)
Rian Josua Masikome (Faculty of Mathematics, Computer Science and Natural Sciences, RWTH Aachen University)

Jurnal Ilmu Komputer dan Informasi
Vol 11, No 2 (2018): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information

Batik fabric is one of the most profound cultural heritage in Indonesia. Hence, continuous research on understanding it is necessary to preserve it. Despite of being one of the most common research task, Batik’s pattern automatic classification still requires some improvement especially in regards to invariance dilemma. Convolutional neural network (ConvNet) is one of deep learning architecture which able to learn data representation by combining local receptive inputs, weight sharing and convolutions in order to solve invariance dilemma in image classification. Using dataset of 2,092 Batik patches (5 classes), the experiments show that the proposed model, which used deep ConvNet VGG16 as feature extractor (transfer learning), achieves slightly better average of 89 ± 7% accuracy than SIFT and SURF-based that achieve 88 ± 10% and 88 ± 8% respectively. Despite of that, SIFT reaches around 5% better accuracy in rotated and scaled dataset.