Recognition of fabric composition of clothing in an image in e-commerce using neural networks
Objectives. Development of new approach for recognizing the fabric composition of clothing in e-commerce images by using generative adversarial network(GAN) to generate synthetic images of clothing with known fabric composition, to be used to train the CNN to classify the fabric composition of real...
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| Format: | Article |
| Language: | Russian |
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National Academy of Sciences of Belarus, the United Institute of Informatics Problems
2023-09-01
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| Series: | Informatika |
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| Online Access: | https://inf.grid.by/jour/article/view/1243 |
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| author | V. V. Sorokina |
| author_facet | V. V. Sorokina |
| author_sort | V. V. Sorokina |
| collection | DOAJ |
| description | Objectives. Development of new approach for recognizing the fabric composition of clothing in e-commerce images by using generative adversarial network(GAN) to generate synthetic images of clothing with known fabric composition, to be used to train the CNN to classify the fabric composition of real clothing images. Instead of a classic clothing image, a copy is generated with the material zoomed to fibers and fabric structure.Methods. The main methods to recognize the fabric composition of the clothing image in the e-commerce are the creation and annotation of a dataset for the neural network training, synthesis of the fabric of clothing, the choice of architecture and its modification, validation and testing, and interpretation of the results.Results. Experimental results with the constructed method show that it is effective for accurately recognizing the fabric composition of e-commerce clothing to be used to improve search and browsing on websites.Conclusion. In the course of the experiment, using a generative adversarial network, a data set of e-commerce products was synthesized and annotated, neural networks were built to recognize the composition of the fabric of clothing items. The results of the study showed that the new approach for recognizing the fabric of clothing provides higher accuracy in comparison with already known methods, in addition, the use of the attention model also gives good results to improve the metrics. |
| format | Article |
| id | doaj-art-402a7f8ee3924f859ca7b67a0096efca |
| institution | Kabale University |
| issn | 1816-0301 |
| language | Russian |
| publishDate | 2023-09-01 |
| publisher | National Academy of Sciences of Belarus, the United Institute of Informatics Problems |
| record_format | Article |
| series | Informatika |
| spelling | doaj-art-402a7f8ee3924f859ca7b67a0096efca2025-02-03T11:40:30ZrusNational Academy of Sciences of Belarus, the United Institute of Informatics ProblemsInformatika1816-03012023-09-01203374910.37661/1816-0301-2023-20-3-37-491040Recognition of fabric composition of clothing in an image in e-commerce using neural networksV. V. Sorokina0Belarusian State UniversityObjectives. Development of new approach for recognizing the fabric composition of clothing in e-commerce images by using generative adversarial network(GAN) to generate synthetic images of clothing with known fabric composition, to be used to train the CNN to classify the fabric composition of real clothing images. Instead of a classic clothing image, a copy is generated with the material zoomed to fibers and fabric structure.Methods. The main methods to recognize the fabric composition of the clothing image in the e-commerce are the creation and annotation of a dataset for the neural network training, synthesis of the fabric of clothing, the choice of architecture and its modification, validation and testing, and interpretation of the results.Results. Experimental results with the constructed method show that it is effective for accurately recognizing the fabric composition of e-commerce clothing to be used to improve search and browsing on websites.Conclusion. In the course of the experiment, using a generative adversarial network, a data set of e-commerce products was synthesized and annotated, neural networks were built to recognize the composition of the fabric of clothing items. The results of the study showed that the new approach for recognizing the fabric of clothing provides higher accuracy in comparison with already known methods, in addition, the use of the attention model also gives good results to improve the metrics.https://inf.grid.by/jour/article/view/1243classification of fabric compositiongenerative adversarial networkconvolutional neural networke-commerceimage synthesisattention model |
| spellingShingle | V. V. Sorokina Recognition of fabric composition of clothing in an image in e-commerce using neural networks Informatika classification of fabric composition generative adversarial network convolutional neural network e-commerce image synthesis attention model |
| title | Recognition of fabric composition of clothing in an image in e-commerce using neural networks |
| title_full | Recognition of fabric composition of clothing in an image in e-commerce using neural networks |
| title_fullStr | Recognition of fabric composition of clothing in an image in e-commerce using neural networks |
| title_full_unstemmed | Recognition of fabric composition of clothing in an image in e-commerce using neural networks |
| title_short | Recognition of fabric composition of clothing in an image in e-commerce using neural networks |
| title_sort | recognition of fabric composition of clothing in an image in e commerce using neural networks |
| topic | classification of fabric composition generative adversarial network convolutional neural network e-commerce image synthesis attention model |
| url | https://inf.grid.by/jour/article/view/1243 |
| work_keys_str_mv | AT vvsorokina recognitionoffabriccompositionofclothinginanimageinecommerceusingneuralnetworks |