Open AccessFeature extraction Hue, Saturation, Value (HSV) and Gray Level Cooccurrence Matrix (GLCM) for identification of woven fabric motifs in South Central Timor Regency
Author(s) -
F S Lesiangi,
Arfan Y Mauko,
Bertha S. Djahi
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2017/1/012010
Subject(s) - woven fabric , artificial intelligence , hue , weaving , hsl and hsv , pattern recognition (psychology) , feature extraction , gray level , feature (linguistics) , computer science , computer vision , mathematics , image (mathematics) , composite material , materials science , virus , virology , biology , linguistics , philosophy
South Central Timor (TTS) is one of the districts that has a weaving culture and also produces woven cloth in East Nusa Tenggara. The many types of woven fabric from each TTS tribe makes outsiders and even native TTS people do not recognize the typical TTS woven fabric, therefore we need a system that can help facilitate the community in recognizing the type and motif of woven fabric. In this study, digital image processing is used to identify the type of woven fabric in the TTS district using the HSV color feature extraction method, and the GLCM texture feature, and to measure the similarity of woven fabric using the Euclidean distance method. The image data of the woven fabric used is the image of woven fabric from 3 tribes of TTS district, namely the Amanatun, Amanuban, and Mollo tribes. Identification of woven fabric motifs using the K-fold cross validation test with two stages, namely the training and testing stages. The results of testing variants using 10 fold get an accuracy rate for GLCM texture features of 55%, for HSV color features of 62.5% and a combination of color and texture features of 91.67%.