Logo design analysis by ranking

Takuro Karamatsu, Daiki Suehiro, Seiichi Uchida

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

1 被引用数 (Scopus)

抄録

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.

本文言語英語
ホスト出版物のタイトルProceedings - 15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019
出版社IEEE Computer Society
ページ1482-1487
ページ数6
ISBN(電子版)9781728128610
DOI
出版ステータス出版済み - 9 2019
イベント15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019 - Sydney, オーストラリア
継続期間: 9 20 20199 25 2019

出版物シリーズ

名前Proceedings of the International Conference on Document Analysis and Recognition, ICDAR
ISSN(印刷版)1520-5363

会議

会議15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019
国/地域オーストラリア
CitySydney
Period9/20/199/25/19

All Science Journal Classification (ASJC) codes

  • コンピュータ ビジョンおよびパターン認識

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引用スタイル