Fashion styles adopted every day are an important aspect of culture, and style trend analysis helps provide a deeper understanding of our societies and cultures. To analyze everyday fashion trends from the humanities perspective, we need a digital archive that includes images of what people wore in their daily lives over an extended period. In fashion research, building digital fashion image archives has attracted significant attention. However, the existing archives are not suitable for retrieving everyday fashion trends. In addition, to interpret how the trends emerge, we need non-fashion data sources relevant to why and how people choose fashion. In this study, we created a new fashion image archive called Chronicle Archive of Tokyo Street Fashion (CAT STREET) based on a review of the limitations in the existing digital fashion archives. CAT STREET includes images showing the clothing people wore in their daily lives during the period 1970--2017, which contain timestamps and street location annotations. We applied machine learning to CAT STREET and found two types of fashion trend patterns. Then, we demonstrated how magazine archives help us interpret how trend patterns emerge. These empirical analyses show our approach's potential to discover new perspectives to promote an understanding of our societies and cultures through fashion embedded in consumers' daily lives.
翻译：每天采用的时装风格是文化的一个重要方面,时装趋势分析有助于更深入地了解我们的社会和文化。为了从人文角度分析日常时装趋势,我们需要一个数字档案,其中包括人们在很长一段时间的日常生活中所穿的图像。在时装研究中,建立数字时装图像档案引起了极大关注。然而,现有的档案并不适合于检索日常时装趋势。此外,为了解释趋势如何出现,我们需要与人们选择时装的原因和方式相关的非时装数据源。在这个研究中,我们创建了一个新的时装图像档案,名为东京街时装纪事档案(CAT STREET),它基于对现有数字时装档案的局限性的回顾。CAT STREET包含人们在1970至2017年期间日常生活中所穿的服装的图像,其中包括时装标志和街道位置说明。我们应用机器学习CAT STREET, 发现了两种时装趋势模式。然后,我们展示了杂志档案如何帮助我们解释趋势模式的形成。这些实证分析显示了我们从日常生活中探索新观点的方法,通过消费者的日常生活和文化促进新认识。
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