Scenery-Based Fashion Recommendation with Cross-Domain Geneartive Adverserial Networks
| Autor: | Jin-Woo Jeong, Sang-Young Jo, Hee-Eun Cho, Sun-Hye Jang |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
Taste (sociology) media_common.quotation_subject Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology Recommender system Domain (software engineering) Visualization High complexity Human–computer interaction 020204 information systems Factor (programming language) 0202 electrical engineering electronic engineering information engineering computer computer.programming_language Natural landscape media_common |
| Zdroj: | BigComp |
| Popis: | To build an effective fashion recommendation system is a still challenging issue due to its high complexity. Previous research works generally have focused on how to provide fashion items visually similar to the user's current fashion taste. However, a scenery (natural landscape) around users is also an important affective factor in recommending fashions. This paper presents a novel system to recommend fashion designs that fit target sceneries. To address this, the exemplar photos regarding the target landscape are first collected from the database. Afterwards, a cross-domain generative adversarial network (GAN) is applied to generate fashion designs from the sceneries. The experimental results demonstrate the feasibility of the proposed system and imply further research directions. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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