Path: Top -> Journal -> Telkomnika -> 2021 -> Vol 19, No 3, June
A comparative analysis of automatic deep neural networks for image retrieval
Oleh : Hanan A. Al-Jubouri, Sawsan M. Mahmmod, Telkomnika
Dibuat : 2021-09-06, dengan 0 file
Keyword : convolutional neural networks, image retrieval deep learning, content-based image retrieval, image classification
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/18157
Sumber pengambilan dokumen : Web
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.
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.
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| Properti | Nilai Properti |
|---|---|
| ID Publisher | gdlhub |
| Organisasi | Telkomnika |
| Nama Kontak | Herti Yani, S.Kom |
| Alamat | Jln. Jenderal Sudirman |
| Kota | Jambi |
| Daerah | Jambi |
| Negara | Indonesia |
| Telepon | 0741-35095 |
| Fax | 0741-35093 |
| E-mail Administrator | elibrarystikom@gmail.com |
| E-mail CKO | elibrarystikom@gmail.com |
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