  The paper presents a novel concept that analyzes and visualizes worldwide
fashion trends. Our goal is to reveal cutting-edge fashion trends without
displaying an ordinary fashion style. To achieve the fashion-based analysis, we
created a new fashion culture database (FCDB), which consists of 76 million
geo-tagged images in 16 cosmopolitan cities. By grasping a fashion trend of
mixed fashion styles,the paper also proposes an unsupervised fashion trend
descriptor (FTD) using a fashion descriptor, a codeword vetor, and temporal
analysis. To unveil fashion trends in the FCDB, the temporal analysis in FTD
effectively emphasizes consecutive features between two different times. In
experiments, we clearly show the analysis of fashion trends and fashion-based
city similarity. As the result of large-scale data collection and an
unsupervised analyzer, the proposed approach achieves world-level fashion
visualization in a time series. The code, model, and FCDB will be publicly
available after the construction of the project page.
