Batik image retrieval using convolutional neural network
By : Heri Prasetyo, Berton Arie Putra Akardihas, Telkomnika
Created : 2020-01-09, with 1 files
Keyword : : autoencoder, CNN, deep learning, feature extraction, image retrieval
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/issue/view/640
Document Source : 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.
Property | Value |
---|---|
Publisher ID | gdlhub |
Organization | Telkomnika |
Contact Name | Herti Yani, S.Kom |
Address | Jln. Jenderal Sudirman |
City | Jambi |
Region | Jambi |
Country | Indonesia |
Phone | 0741-35095 |
Fax | 0741-35093 |
Administrator E-mail | elibrarystikom@gmail.com |
CKO E-mail | elibrarystikom@gmail.com |
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