GDL

Journal from gdlhub / 2020-01-09 11:36:30
Oleh : Heri Prasetyo, Berton Arie Putra Akardihas, Telkomnika
Dibuat : 2020-01-09, dengan 1 file

Keyword : : autoencoder, CNN, deep learning, feature extraction, image retrieval
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/issue/view/640
Sumber pengambilan dokumen : web

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.

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