Recognition of fabric composition of clothing in an image in e-commerce using neural networks
https://doi.org/10.37661/1816-0301-2023-20-3-37-49
Abstract
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.
About the Author
V. V. Sorokina
Belarusian State University
Belarus
Belarus
Viktoria V. Sorokina - Postgraduate Student of web-Technologies and Computer Modeling Department of Mechanics and Mathematics Faculty, Belarusian State University.
Nezavisimosti av., 4, Minsk, 220050
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For citations:
Sorokina V.V. Recognition of fabric composition of clothing in an image in e-commerce using neural networks. Informatics. 2023;20(3):37-49. (In Russ.) https://doi.org/10.37661/1816-0301-2023-20-3-37-49
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