Open AccessExpert role in image classification using CNN for hard to identify object: distinguishing batik and its imitation
Author(s) -
Zohanto Widyantoko,
Titik Purwati Widowati,
Isnaini Isnaini,
Paras Trapsiladi
Publication year - 2021
Publication title -
iaes international journal of artificial intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.341
H-Index - 7
eISSN - 2252-8938
pISSN - 2089-4872
DOI - 10.11591/ijai.v10.i1.pp93-100
Subject(s) - imitation , computer science , artificial intelligence , object (grammar) , image (mathematics) , process (computing) , computer vision , product (mathematics) , span (engineering) , pattern recognition (psychology) , mathematics , psychology , social psychology , civil engineering , geometry , engineering , operating system
In this research we try to solve the recognition problem in differentiating between batik and its imitation. Batik is an Indonesian heritage of process in making traditional textile product that is now endangered by the existence of imitation products. We try to compare two popular CNN model to classify batik products into five classes. The classes are tulis, cap, print warna, print malam, cabut warna. Tulis and cap are genuine batik, and the other three are an imitation. We realize that this problem is go beyond the recognition of fine grained image problem, it is a hard to identify image problem because even the batik experts is having a hard time identifying batik and its imitation if only based on its picture. The two CNN models, inceptionV3 and mobilenetV2 were trained on three types of image. One type is a freely taken image, the other two were taken based on the experts suggestion. The accuracy score shows that the model trained with the suggestion based picture perform better than the one trained with the random picture.