@inproceedings{4fad8101cf0b43c3844c47ecc2ffbd30,
title = "Classification of textile image using support vector machine with textural feature",
abstract = "Fabrics have different materials, colors, and texture. The development of fashion is influenced by climate and fashion trends. In the particular climate of the summer for example, a floral pattern clothes are interesting and in other climates, the other fashion patterns, such as lines, polka dot, and strips might be more interesting. This study focuses on textile image classification based on its texture. Each texture in textile image has a particular characteristic that can distinguish with other motifs. The feature extraction method used in this study are Gray Level Co-occurrence Matrix (GLCM), Linear Binary Pattern (LBP), and a Moment Invariant (MI). Furthermore, all texture feature are then reduced using Principal Component Analysis (PCA). The experiment shows that the best result can be achieved using combination of GLCM and LBP features with accuracy 74.15% using linear kernel SVM.",
keywords = "GLCM, LBP, SVM, classification, feature texture, moment invariant, textile",
author = "Pawening, {Ratri Enggar} and Rohman Dijaya and Thomas Brian and Nanik Suciati",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; International Conference on Information and Communication Technology and Systems, ICTS 2015 ; Conference date: 16-09-2015",
year = "2016",
month = jan,
day = "12",
doi = "10.1109/ICTS.2015.7379883",
language = "English",
series = "Proceedings of 2015 International Conference on Information and Communication Technology and Systems, ICTS 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "119--122",
booktitle = "Proceedings of 2015 International Conference on Information and Communication Technology and Systems, ICTS 2015",
address = "United States",
}