Garment Fit Evaluation Using Machine Learning Technology
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Date
2018
Journal Title
Journal ISSN
Volume Title
Publisher
Springer
Abstract
Presently, garment fit evaluation mainly focuses on real try-on and rarely
deals with virtual try-on.With the rapid development of e-commerce, there is a profound
growth of garment purchases through the Internet. In this context, fit evaluation
of virtual garment try-on is vital in the clothing industry. In this chapter, we propose a
Naive Bayes-based model to evaluate garment fit. The inputs of the proposed model
are digital clothing pressures of different body parts, generated from a 3D garment
CAD software, while the output is the predicted result of garment fit (fit or unfit).
To construct and train the proposed model, data on digital clothing pressures and
garment real fit was collected for input and output learning data, respectively. By
learning from these data, our proposed model can predict garment fit rapidly and
automatically without any real try-on; therefore, it can be applied to remote garment
fit evaluation in the context of e-shopping. Finally, the effectiveness of our
proposed method was validated using a set of test samples. Test results showed that
digital clothing pressure is a better index than ease allowance to evaluate garment fit,
and machine learning-based garment fit evaluation methods have higher prediction
accuracies.
Description
Keywords
Digital clothing pressure, Support vector machines, Naive Bayes Active learning, Ease allowance, Real try-on
Citation
Liu, K., Zeng, X., Bruniaux, P., Tao, X., Kamalha, E., & Wang, J. (2018). Garment fit evaluation using machine learning technology. In Artificial Intelligence for Fashion Industry in the Big Data Era (pp. 273-288). Springer, Singapore. https://doi.org/10.1007/978-981-13-0080-6_14