Batik image retrieval using convolutional neural network

Heri Prasetyo, Berton Arie Putra Akardihas


This paper presents a simple technique for performing Batik image retrieval using the Convolutional Neural Network (CNN) approach. Two CNN models, i.e. supervised and unsupervised learning approach, are considered to perform end-to-end feature extraction in order to describe the content of Batik image. The distance metrics measure the similarity between the query and target images in database based on the feature generated from CNN architecture. As reported in the experimental section, the proposed supervised CNN model achieves better performance compared to unsupervised CNN in the Batik image retrieval system. In addition, image feature composed from the proposed CNN model yields better performance compared to that of the handcrafted feature descriptor. Yet, it demonstrates the superiority performance of deep learning-based approach in the Batik image retrieval system.


autoencoder; CNN; deep learning; feature extraction; image retrieval;

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TELKOMNIKA Telecommunication, Computing, Electronics and Control
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