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.
Highlights
Multi-style features determine the diversity of wearing so that the traditional fixed size garments are unable to meet the requirements of the garment fit.[1]
With the rapid transformation of the fashion industry, big data-driven technology presents an efficient and aggressive development momentum, which penetrates into the traditional industry and spreads to the field of garment customization.[4]
The application of garment customization is becoming more and more wider, which leads to the strong demand for diversified data such as the body size, the type of personalized, and high-end consumer demand provides a huge customized space.[6]
Summary
Multi-style features determine the diversity of wearing so that the traditional fixed size garments are unable to meet the requirements of the garment fit.[1] The characteristics of garment fitness put forward higher requirements to garment confortability.[2] With regard to consumers’ increasingly individualized wearing demand, strict quality requirements will be a chance to promote the pursuit of high-end consumption psychology going on in spending to bring about the transformation of seller’s market to buyer’s3 market. It needs to build the customized database of local customer storage and indexing, which performs with customized data resources fully and effectively that can be shared between the consumers and the customized team, resulting in improved performance over previous existing account scheme
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