Garment customization big data–processing and analysis in optimization design

Abstract

In order to adapt to the expansion and transformation of the garment customization, big data is increasingly used in the online customization process. The aim of our research was to propose a method of tailoring clothing throughout the early stages of personal design and product development. This approach improves the understanding of garment fitting by analyzing individual preferences, and also helps designers capture user needs more quickly and deal with them more accurately. Our approach is built upon garment customization using unsupervised approach to learning visual compatibility from clothing data sets. For the garment definition, a competitive analysis was made to identify garment custom process. Then, training model was applied in personal customization environment while examining the links through machine learning module. Indeed, garment customization with big data provides new insights into garment customization, in terms of effectively optimizing the combination of mix-and-match clothing choices as well as generative learning of fashion design.