GMRNet: A Novel Geometric Mean Relation Network for Few-Shot Very Similar Object Classification
With the widespread use of Deep Learning (DL), the use of DL has increased to provide a solution to the problem of object recognition and classification. In addition to classifying many different types of objects, the Deep Metrics Learning(DML) technique is effective in classifying objects that are...
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ftdoajarticles:oai:doaj.org/article:3a11628824d24c2ebed3b906a035662a 2023-05-15T16:01:47+02:00 GMRNet: A Novel Geometric Mean Relation Network for Few-Shot Very Similar Object Classification Canan Tastimur Erhan Akin 2022-01-01T00:00:00Z https://doi.org/10.1109/ACCESS.2022.3206528 https://doaj.org/article/3a11628824d24c2ebed3b906a035662a EN eng IEEE https://ieeexplore.ieee.org/document/9889728/ https://doaj.org/toc/2169-3536 2169-3536 doi:10.1109/ACCESS.2022.3206528 https://doaj.org/article/3a11628824d24c2ebed3b906a035662a IEEE Access, Vol 10, Pp 97360-97369 (2022) Classification deep metric learning few shot learning relation network Electrical engineering. Electronics. Nuclear engineering TK1-9971 article 2022 ftdoajarticles https://doi.org/10.1109/ACCESS.2022.3206528 2022-12-30T22:03:22Z With the widespread use of Deep Learning (DL), the use of DL has increased to provide a solution to the problem of object recognition and classification. In addition to classifying many different types of objects, the Deep Metrics Learning(DML) technique is effective in classifying objects that are visually very similar to each other. In this study, a novel Relation Network (RN) based DML has been designed to classify objects in two different datasets we created. We distinguished groups of objects that had a high degree of similarity to each other. These objects have been categorized using few-shot learning(FSL) since they are quite similar to one another. The impact of changing the number of classes and samples in the database on the network’s performance has been studied. It is shown how the network’s accuracy varies depending on the N-way (number of classes) and K-shots (number of samples) combinations used in its design. Additionally, the performance of the network has improved by an average of 15% thanks to the contribution of the recently introduced geometric mean module to the RN in our study. The accuracy rate of our recommended RN in screw and spare parts datasets is 96.1% and 92.3%, respectively. The first dataset consists of 1800 screw images with 18 classes, while the second dataset consists of 4100 spare parts images with 20 classes. The effectiveness of our method is expressed by the two datasets that we have extensively experimentally studied. Article in Journal/Newspaper DML Directory of Open Access Journals: DOAJ Articles IEEE Access 10 97360 97369 |
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Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
language |
English |
topic |
Classification deep metric learning few shot learning relation network Electrical engineering. Electronics. Nuclear engineering TK1-9971 |
spellingShingle |
Classification deep metric learning few shot learning relation network Electrical engineering. Electronics. Nuclear engineering TK1-9971 Canan Tastimur Erhan Akin GMRNet: A Novel Geometric Mean Relation Network for Few-Shot Very Similar Object Classification |
topic_facet |
Classification deep metric learning few shot learning relation network Electrical engineering. Electronics. Nuclear engineering TK1-9971 |
description |
With the widespread use of Deep Learning (DL), the use of DL has increased to provide a solution to the problem of object recognition and classification. In addition to classifying many different types of objects, the Deep Metrics Learning(DML) technique is effective in classifying objects that are visually very similar to each other. In this study, a novel Relation Network (RN) based DML has been designed to classify objects in two different datasets we created. We distinguished groups of objects that had a high degree of similarity to each other. These objects have been categorized using few-shot learning(FSL) since they are quite similar to one another. The impact of changing the number of classes and samples in the database on the network’s performance has been studied. It is shown how the network’s accuracy varies depending on the N-way (number of classes) and K-shots (number of samples) combinations used in its design. Additionally, the performance of the network has improved by an average of 15% thanks to the contribution of the recently introduced geometric mean module to the RN in our study. The accuracy rate of our recommended RN in screw and spare parts datasets is 96.1% and 92.3%, respectively. The first dataset consists of 1800 screw images with 18 classes, while the second dataset consists of 4100 spare parts images with 20 classes. The effectiveness of our method is expressed by the two datasets that we have extensively experimentally studied. |
format |
Article in Journal/Newspaper |
author |
Canan Tastimur Erhan Akin |
author_facet |
Canan Tastimur Erhan Akin |
author_sort |
Canan Tastimur |
title |
GMRNet: A Novel Geometric Mean Relation Network for Few-Shot Very Similar Object Classification |
title_short |
GMRNet: A Novel Geometric Mean Relation Network for Few-Shot Very Similar Object Classification |
title_full |
GMRNet: A Novel Geometric Mean Relation Network for Few-Shot Very Similar Object Classification |
title_fullStr |
GMRNet: A Novel Geometric Mean Relation Network for Few-Shot Very Similar Object Classification |
title_full_unstemmed |
GMRNet: A Novel Geometric Mean Relation Network for Few-Shot Very Similar Object Classification |
title_sort |
gmrnet: a novel geometric mean relation network for few-shot very similar object classification |
publisher |
IEEE |
publishDate |
2022 |
url |
https://doi.org/10.1109/ACCESS.2022.3206528 https://doaj.org/article/3a11628824d24c2ebed3b906a035662a |
genre |
DML |
genre_facet |
DML |
op_source |
IEEE Access, Vol 10, Pp 97360-97369 (2022) |
op_relation |
https://ieeexplore.ieee.org/document/9889728/ https://doaj.org/toc/2169-3536 2169-3536 doi:10.1109/ACCESS.2022.3206528 https://doaj.org/article/3a11628824d24c2ebed3b906a035662a |
op_doi |
https://doi.org/10.1109/ACCESS.2022.3206528 |
container_title |
IEEE Access |
container_volume |
10 |
container_start_page |
97360 |
op_container_end_page |
97369 |
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1766397511467008000 |