Batik Banyumasan 5148

Published: 3 June 2024| Version 1 | DOI: 10.17632/vsjrkhs9km.1
Contributor:
Mohamad Rizal Syafi'i

Description

The 'Batik Banyumasan 5148' dataset is a data set containing 5.148 images of typical batik motifs from Banyumas, a district in Central Java, Indonesia which is famous for its rich culture, including batik art. The data collected was in the form of digital images of Banyumasan batik with 11 different motifs, namely Angguran, Ayam Puger, Jahe Lumbon, Jahe Puger, Jahe Srimpang, Lumbon, Madu Bronto, Pring Sedapur, Puger Galar, Puger Telu Bal, and Wit Lumbon. This dataset was developed for research and development of pattern recognition technology, image analysis, image classification and preservation of Banyumasan batik culture.

Files

Steps to reproduce

Image data was collected by taking photos directly using a smartphone camera that has 64MP Wide Angle Camera specifications, sensor size 1/1.97", pixel size 0.7 μm, and Aperture f/1.79 from Rumah Batik R, Sokaraja, Banyumas. Photo taken by spreading the batik cloth on the floor using normal photo mode without any additional features with a 1:1 ratio. Apart from that, the photo was taken during the day in an open room, exposed to sunlight, and there was no light labelling on the image data confirmed directly by Mr Heru Santoso as the owner of Rumah Batik R, so it can be accounted for. In this dataset, an augmentation process is also applied to each image data. This aims to increase the quantity and diversity of data in the dataset. The augmentation process uses one of the Python libraries, namely Albumementations. There are eight image data augmentation parameters used, namely channel shuffle, elastic transform, flip, fourier domain adaptation, random grid shuffle, random rotate 90, sharpen, and transpose.

Institutions

Institut Teknologi Telkom Purwokerto

Categories

Engineering, Computer Vision, Cultural Heritage, Image Processing, Machine Learning, Image Retrieval, Informatics, Image Classification, Heritage, Deep Learning, Image Analysis, Generative Adversarial Network, Data Augmentation

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