TY - GEN
T1 - Fast discrete curvelet transform and HSV color features for batik image clansificotlon
AU - Suciati, Nanik
AU - Kridanto, Agri
AU - Naufal, Mohammad Farid
AU - Machmud, Muhammad
AU - Wicaksono, Ardian Yusuf
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/1/12
Y1 - 2016/1/12
N2 - Batik is one of the cultural heritages in Indonesia. Batik has many types spread around Indonesia. Related to the diversity of batik, an effort to develop a database to preserve batik information is required. Searching batik information from the database by using keywords such as the province name where a batik came from, sometimes is difficult. In some cases, people only has a batik image without knowing any additional information, such as motif name and it's origin. Attaching a modul to classify batik image automatically into the database will be very useful, so that people can search more information about batik by inputting a batik image. This research proposes batik image classification using Fast Discrete Curvelet Transform (FDCT) and Hue Saturation Value (HSV) space as the representation of texture and color features, and K Nearest Neighbour (KNN) as the classifier. The experiment give a good result, which is showed by the worst classification error rate 3.33% for combined features vector.
AB - Batik is one of the cultural heritages in Indonesia. Batik has many types spread around Indonesia. Related to the diversity of batik, an effort to develop a database to preserve batik information is required. Searching batik information from the database by using keywords such as the province name where a batik came from, sometimes is difficult. In some cases, people only has a batik image without knowing any additional information, such as motif name and it's origin. Attaching a modul to classify batik image automatically into the database will be very useful, so that people can search more information about batik by inputting a batik image. This research proposes batik image classification using Fast Discrete Curvelet Transform (FDCT) and Hue Saturation Value (HSV) space as the representation of texture and color features, and K Nearest Neighbour (KNN) as the classifier. The experiment give a good result, which is showed by the worst classification error rate 3.33% for combined features vector.
KW - Fast Discrete Curvelet Tcansform (FDCT)
KW - HSV
KW - batik
KW - image classification
UR - http://www.scopus.com/inward/record.url?scp=84964949978&partnerID=8YFLogxK
U2 - 10.1109/ICTS.2015.7379879
DO - 10.1109/ICTS.2015.7379879
M3 - Conference contribution
AN - SCOPUS:84964949978
T3 - Proceedings of 2015 International Conference on Information and Communication Technology and Systems, ICTS 2015
SP - 99
EP - 103
BT - Proceedings of 2015 International Conference on Information and Communication Technology and Systems, ICTS 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - International Conference on Information and Communication Technology and Systems, ICTS 2015
Y2 - 16 September 2015
ER -