Name
Abstract | ||
|---|---|---|
In this paper, we analyze logo designs by using machine learning, as a promising trial of graphic design analysis. Specifically, we will focus on favicon images, which are tiny logos used as company icons on web browsers, and analyze them to understand their trends in individual industry classes. For example, if we can catch the subtle trends in favicons of financial companies, they will suggest to us how professional designers express the atmosphere of financial companies graphically. For the purpose, we will use top-rank learning, which is one of the recent machine learning methods for ranking and very suitable for revealing the subtle trends in graphic designs. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICDAR.2019.00238 | ICDAR |
Field | DocType | Citations |
Computer vision,World Wide Web,Favicon,Web browser,Ranking,Computer science,Logos Bible Software,Logo,Graphic design,Artificial intelligence | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Takuro Karamatsu | 1 | 0 | 0.68 |
| Daiki Suehiro | 2 | 18 | 4.72 |
| Seiichi Uchida | 3 | 790 | 105.59 |