TY - JOUR
T1 - Fashion intelligence system
T2 - An outfit interpretation utilizing images and rich abstract tags
AU - Shimizu, Ryotaro
AU - Saito, Yuki
AU - Matsutani, Megumi
AU - Goto, Masayuki
N1 - Funding Information:
This work was supported by JSPS, Japan KAKENHI Grant Number 21H04600 .
Publisher Copyright:
© 2022 The Author(s)
PY - 2023/3/1
Y1 - 2023/3/1
N2 - 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.
AB - 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.
KW - Attribute activation map
KW - Fashion intelligence
KW - Fashion interpretation
KW - Image retrieval
KW - Outfit image
KW - Visual-semantic embedding
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U2 - 10.1016/j.eswa.2022.119167
DO - 10.1016/j.eswa.2022.119167
M3 - Article
AN - SCOPUS:85142137215
SN - 0957-4174
VL - 213
JO - Expert Systems with Applications
JF - Expert Systems with Applications
M1 - 119167
ER -