| Journal Title /掲載ジャーナル名 |
Expert Systems with Applications |
| Publication Year and Month /掲載年月 |
November, 2022 |
| Paper Title /論文タイトル |
Fashion Intelligence system:An Outfit Interpretation Utilizing Images and Rich Abstruct Tags |
| DOI /論文DOI |
10.1016/j.eswa.2022.119167 |
| Author of Waseda University /本学の著者 |
SHIMIZU, Ryotaro(2nd Year Doctoral Student, Faculty of Science and Engineering, School of Creative Science and Engineering):First Author, Corresponding Author |
| Related Websites /関連Web |
– |
| Abstract /抄録 |
In recent years, it has become common for consumers to familiarize themselves with the latest fashion trends through the internet and engage in their own fashion-inspired shopping activities. Therefore, making fashion-inspired shopping and browsing activities (internet surfing in the fashion domain) comfortable is essential because it leads to interactions in the fashion industry. However, fashion is a fuzzy and complex domain that contains many abstract elements, and this ambiguity and complexity can hinder users’ deep interest in the fashion industry. Therefore, we define a novel technology and domain called “fashion intelligence” and propose a system based on a visual-semantic embedding method for automatically learning and interpreting fashion and obtaining answers to users’ questions. Our proposed method can embed the abundant abstract tag information in the same projective space as outfit images. Mapping of images and tags in a projective space helps search for outfit images using fashion-specific abstract words. In addition, visually estimating the degree of relevance between images and tags helps interpret abstract words. As a result, this research helps decrease fashion-specific ambiguity and complexity and supports the marketing activities and fashion choices of both experts and non-experts. |
