Garment Fit Evaluation Using Machine Learning Technology

Simple item page
dc.contributor.authorLiu, Kaixuan
dc.contributor.authorZeng, Xianyi
dc.contributor.authorBruniaux, Pascal
dc.contributor.authorTao, Xuyuan
dc.contributor.authorKamalha, Edwin
dc.contributor.authorWang, Jianping
dc.date.accessioned2022-12-04T13:11:27Z
dc.date.available2022-12-04T13:11:27Z
dc.date.issued2018
dc.description.abstractPresently, 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.en_US
dc.identifier.citationLiu, 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_14en_US
dc.identifier.urihttps://doi.org/10.1007/978-981-13-0080-6_14
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/5826
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.subjectDigital clothing pressureen_US
dc.subjectSupport vector machinesen_US
dc.subjectNaive Bayes Active learningen_US
dc.subjectEase allowanceen_US
dc.subjectReal try-onen_US
dc.titleGarment Fit Evaluation Using Machine Learning Technologyen_US
dc.typeArticleen_US
Files
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Engineering and Technology