DEVELOPMENT OF A NIGERIAN CULTURAL ATTIRE RECOGNITION SYSTEM
This paper presents the development of a system to automatically recognize Nigerian traditional attire worn by major ethnic groups. Samples of clothing images depicting the cultural groups were obtained from publicly available online sources and an architecture to classify the images classes was de...
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Format: | Article |
Language: | English |
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Alma Mater Publishing House "Vasile Alecsandri" University of Bacau
2023-09-01
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Series: | Journal of Engineering Studies and Research |
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Online Access: | https://jesr.ub.ro/index.php/1/article/view/377 |
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author | OGUNBIYI DIMPLE JIMOH KUDIRAT ADEBAYO OLAJIDE OKELOLA LOVELYN |
author_facet | OGUNBIYI DIMPLE JIMOH KUDIRAT ADEBAYO OLAJIDE OKELOLA LOVELYN |
author_sort | OGUNBIYI DIMPLE |
collection | DOAJ |
description |
This paper presents the development of a system to automatically recognize Nigerian traditional attire worn by major ethnic groups. Samples of clothing images depicting the cultural groups were obtained from publicly available online sources and an architecture to classify the images classes was designed using the Convolutional Neural Network (CNN) model. Performance evaluation results from experiment show that the model can classify images accordingly, achieving a validation accuracy score of 86 %. A web application interface was also implemented to validate the model’s accuracy which shows good potential when integrated in commercial clothing applications.
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format | Article |
id | doaj-art-69b38d5842284de9b05ada8ebffdf55e |
institution | Kabale University |
issn | 2068-7559 2344-4932 |
language | English |
publishDate | 2023-09-01 |
publisher | Alma Mater Publishing House "Vasile Alecsandri" University of Bacau |
record_format | Article |
series | Journal of Engineering Studies and Research |
spelling | doaj-art-69b38d5842284de9b05ada8ebffdf55e2025-02-11T11:39:21ZengAlma Mater Publishing House "Vasile Alecsandri" University of BacauJournal of Engineering Studies and Research2068-75592344-49322023-09-01292DEVELOPMENT OF A NIGERIAN CULTURAL ATTIRE RECOGNITION SYSTEMOGUNBIYI DIMPLEJIMOH KUDIRATADEBAYO OLAJIDEOKELOLA LOVELYN This paper presents the development of a system to automatically recognize Nigerian traditional attire worn by major ethnic groups. Samples of clothing images depicting the cultural groups were obtained from publicly available online sources and an architecture to classify the images classes was designed using the Convolutional Neural Network (CNN) model. Performance evaluation results from experiment show that the model can classify images accordingly, achieving a validation accuracy score of 86 %. A web application interface was also implemented to validate the model’s accuracy which shows good potential when integrated in commercial clothing applications. https://jesr.ub.ro/index.php/1/article/view/377recognition system, Nigerian attire, convolution neural network |
spellingShingle | OGUNBIYI DIMPLE JIMOH KUDIRAT ADEBAYO OLAJIDE OKELOLA LOVELYN DEVELOPMENT OF A NIGERIAN CULTURAL ATTIRE RECOGNITION SYSTEM Journal of Engineering Studies and Research recognition system, Nigerian attire, convolution neural network |
title | DEVELOPMENT OF A NIGERIAN CULTURAL ATTIRE RECOGNITION SYSTEM |
title_full | DEVELOPMENT OF A NIGERIAN CULTURAL ATTIRE RECOGNITION SYSTEM |
title_fullStr | DEVELOPMENT OF A NIGERIAN CULTURAL ATTIRE RECOGNITION SYSTEM |
title_full_unstemmed | DEVELOPMENT OF A NIGERIAN CULTURAL ATTIRE RECOGNITION SYSTEM |
title_short | DEVELOPMENT OF A NIGERIAN CULTURAL ATTIRE RECOGNITION SYSTEM |
title_sort | development of a nigerian cultural attire recognition system |
topic | recognition system, Nigerian attire, convolution neural network |
url | https://jesr.ub.ro/index.php/1/article/view/377 |
work_keys_str_mv | AT ogunbiyidimple developmentofanigerianculturalattirerecognitionsystem AT jimohkudirat developmentofanigerianculturalattirerecognitionsystem AT adebayoolajide developmentofanigerianculturalattirerecognitionsystem AT okelolalovelyn developmentofanigerianculturalattirerecognitionsystem |