TY - GEN
T1 - Performance comparison analysis features extraction methods for Batik recognition
AU - Nurhaida, Ida
AU - Manurung, Ruli
AU - Arymurthy, Aniati Murni
PY - 2012
Y1 - 2012
N2 - Batik, as a cultural heritage from Indonesia, has a lot of motifs based on certain patterns. This paper discusses feature extraction methods for the recognition of batik motifs in digital images. In this study, the use of several feature extraction methods have been compared in terms of their performance with several scenarios for testing level accuracy. The methods include Gray Level Co-occurrence Matrices (GLCM), Canny Edge Detection, and Gabor filters. The experimental results show that the use of GLCM features has performed the best with a classification accuracy reaching 80%.
AB - Batik, as a cultural heritage from Indonesia, has a lot of motifs based on certain patterns. This paper discusses feature extraction methods for the recognition of batik motifs in digital images. In this study, the use of several feature extraction methods have been compared in terms of their performance with several scenarios for testing level accuracy. The methods include Gray Level Co-occurrence Matrices (GLCM), Canny Edge Detection, and Gabor filters. The experimental results show that the use of GLCM features has performed the best with a classification accuracy reaching 80%.
UR - http://www.scopus.com/inward/record.url?scp=84875111351&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84875111351
SN - 9789791421157
T3 - 2012 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2012 - Proceedings
SP - 207
EP - 212
BT - 2012 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2012 - Proceedings
T2 - 2012 4th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2012
Y2 - 1 December 2012 through 2 December 2012
ER -