Neural Style Transfer and Clothes Segmentation for Creating New Batik Patterns on Clothing Design

Abstract

Purpose: Applying the original batik image style to other object images and generating new batik patterns that applied to clothing.

Methods: This research uses the Neural Style Transfer method to apply object images to batik to produce new batik patterns, and Clothes Segmentation is used to select areas of clothing in the image so the new batik patterns can be applied to clothing images. And Testing using SSIM, LPIPS and PSNR metrics. This research uses Google Colab, batik image data, and clothing mockup images taken from the internet.

Result: This study shows high average results on SSIM, LPIPS and fair results on PSNR. The results show that the similarity is relatively high with high detected noise.

Novelty: This research develops a new approach in the field of batik pattern innovation and its application to clothing design images. The novelty of this research lies in the implementation of Neural Style Transfer and Clothes Segmentation, which results in a method of exploring new batik patterns and applying them to clothing design images.