TELKOMNIKA Telecommunication, Computing, Electronics and Control
A comparative analysis of automatic deep neural networks for image retrieval

TELKOMNIKA Telecommunication, Computing, Electronics and Control
A comparative analysis of automatic deep neural networks for image retrieval

Content-based image retrieval
Convolutional neural networks
Image classification
Image retrieval deep learning

Feature descriptor and similarity measures are the two core components in
content-based image retrieval and crucial issues due to “semantic gap”
between human conceptual meaning and a machine low-level feature.
Recently, deep learning techniques have shown a great interest in image
recognition especially in extracting features information about the images. In
this paper, we investigated, compared, and evaluated different deep
convolutional neural networks and their applications for image classification
and automatic image retrieval. The approaches are: simple convolutional
neural network, AlexNet, GoogleNet, ResNet-50, Vgg-16, and Vgg-19. We
compared the performance of the different approaches to prior works in this
domain by using known accuracy metrics and analyzed the differences
between the approaches. The performances of these approaches are
investigated using public image datasets corel 1K, corel 10K, and Caltech 256.
Hence, we deduced that GoogleNet approach yields the best overall results. In
addition, we investigated and compared different similarity measures. Based
on exhausted mentioned investigations, we developed a novel algorithm for
image retrieval.

Hanan A. Al-Jubouri, Sawsan M. Mahmmod

http://journal.uad.ac.id/index.php/TELKOMNIKA

Sep 20, 2020

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