| Title |
Computer vision-based multi-class classification of garments using a three-level hierarchical approach / |
| Authors |
Kvedaraite, Asta ; Buineviciute, Neda ; Paulauskaite-Taraseviciene, Agne |
| Full Text |
|
| Is Part of |
CEUR workshop proceedings: IVUS 2023: Information society and university studies 2023: proceedings of the 28th international conference on information society and university studies (IVUS 2023) Kaunas, Lithuania, May 12, 2023 / edited by: A. Lopata, T. Krilavičius, I. Veitaitė, A. García-Holgado.. Aachen : CEUR-WS. 2023, vol. 3575, p. 75-82.. ISSN 1613-0073 |
| Keywords [eng] |
hierarchical model ; multi-class problem ; garment classification ; deep learning |
| Abstract [eng] |
Manually collecting and measuring garment data can be a complex and time-consuming process, including garment classification, which can be a difficult task even for humans. Computer vision algorithms can be trained to classify clothes by analysing large amounts of data and identifying patterns and features specific to each class. A 3-level hierarchical garment classification model has been proposed in the paper, which classifies garments into 3, 8 and 21 classes. The model has been tested with three deep learning architectures LeNet5, AlexNet and sequential CNN model. The results obtained show that the hierarchical approach has the greatest advantage when classifying garments into three and eight classes, and allows an improvement of up to 28%. |
| Published |
Aachen : CEUR-WS |
| Type |
Conference paper |
| Language |
English |
| Publication date |
2023 |
| CC license |
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