Changing Fashion Cultures
This work addresses the need for uncovering cutting-edge fashion trends without ordinary styles, which is incremental as it builds on existing fashion analysis methods with new data and descriptors.
The paper tackled the problem of analyzing and visualizing worldwide fashion trends by creating a new fashion culture database with 76 million geo-tagged images and proposing an unsupervised fashion trend descriptor, achieving world-level fashion visualization in a time series.
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.