On 2 October 2009, UNESCO announced that batik is one of the intangible cultural heritages of humanity. Batik can be defined as a traditional method to write some patterns and dots on fabric and other material. Batik patterns consist of one or more motifs which are repeatedly written in an orderly sequence or a disorderly sequence. CDH
Batik Dataset is collected by capturing 50 types of Batik cloth. Each cloth is captured to as much as six random images and then resized to 128x128 pixels size in JPEG format. Total number of images in a dataset is 300 and consists of 50 classes. In general, there are two patterns of captured batik images, geometric and non-geometric patterns. Dataset provided by Laboratorium Komputasi Cerdas dan Visi, Institut Teknologi Sepuluh Nopember http://kcv.if.its.ac.id
Minarno, A. E. et al. (2018). Comparison of Classification Method for Batik Classification Using Multi Texton Histogram. TELKOMNIKA (Telecommunication Computing Electronics and Control), 16
Minarno, A. E., Munarko, Y., Kurniawardhani, A., & Bimantoro, F. (2016, January). Classification of Texture Using Multi Texton Histogram and Probabilistic Neural Network. In IOP Conference Series: Materials Science and Engineering (Vol. 105, No. 1, p. 012022). IOP Publishing.
Minarno, A. E., Munarko, Y., Kurniawardhani, A., Bimantoro, F., & Suciati, N. (2014, May). Texture feature extraction using co-occurrence matrices of sub-band image for batik image classification. In Information and Communication Technology (ICoICT), 2014 2nd International Conference on (pp. 249-254). IEEE.
Minarno, A. E., & Suciati, N. (2014). Batik image retrieval based on color difference histogram and gray level co-occurrence matrix. TELKOMNIKA (Telecommunication Computing Electronics and Control), 12(3), 597-604.
Minarno, A. E., Munarko, Y., Bimantoro, F., Kurniawardhani, A., & Suciati, N. (2014, February). Batik image retrieval based on enhanced micro-structure descriptor. In Computer Aided System Engineering (APCASE), 2014 Asia-Pacific Conference on (pp. 65-70). IEEE.
Minarno, A. E., & Suciati, N. (2014). IMAGE RETRIEVAL USING MULTI TEXTON CO-OCCURRENCE DESCRIPTOR. Journal of Theoretical & Applied Information Technology, 67(1).
Minarno, A. E., Kurniawardhani, A., & Bimantoro, F. (2016). Image Retrieval Based on Multi Structure Co-occurrence Descriptor. TELKOMNIKA (Telecommunication Computing Electronics and Control), 14(3), 1175-1182
Minarno, A. E., Munarko, Y., Kurniawardhani, A., & Bimantoro, F. (2016, January). Classification of Texture Using Multi Texton Histogram and Probabilistic Neural Network. In IOP Conference Series: Materials Science and Engineering (Vol. 105, No. 1, p. 012022). IOP Publishing.
Minarno, A. E. (2018). Comparison of Classification Method for Batik Classification Using Multi Texton Histogram. TELKOMNIKA (Telecommunication Computing Electronics and Control), 16.
Kurniawardhani, A., Minarno, A. E., & Bimantoro, F. (2016, October). Efficient texture image retrieval of improved completed robust local binary pattern. In Advanced Computer Science and Information Systems (ICACSIS), 2016 International Conference on (pp. 492-497). IEEE.
