Automatic color palette design using color image and sentiment analysis

Ya Fang Peng, Tzren Ru Chou

研究成果: 書貢獻/報告類型會議貢獻

摘要

Designers are often forced to speculate on color combinations and other requirements from customers' abstract and sometimes contradictory descriptions. Thus, too much time is spent on collaboration and modification. Therefore, in this paper a method of combining color imagery with sentiment analysis is proposed to automatically convert colloquial descriptions into color palettes. The algorithm comprises four steps. First, it defines affect words as the basis for text classification. In this study affect words are CIS image words. Second, it collects relevant text corpora from Google and Wikipedia. Third, via model training, it applies word2vec (a word-embedding model) to calculate the lexical affinity of affect words and colors. Furthermore, a prototype system is built to demonstrate its efficacy for automatic color palette design.

原文英語
主出版物標題2019 IEEE 4th International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2019
發行者Institute of Electrical and Electronics Engineers Inc.
頁面389-392
頁數4
ISBN(電子)9781728114095
DOIs
出版狀態已發佈 - 2019 四月
對外發佈Yes
事件4th IEEE International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2019 - Chengdu, 中国
持續時間: 2019 四月 122019 四月 15

出版系列

名字2019 IEEE 4th International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2019

會議

會議4th IEEE International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2019
國家中国
城市Chengdu
期間19/4/1219/4/15

ASJC Scopus subject areas

  • Software

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  • 引用此

    Peng, Y. F., & Chou, T. R. (2019). Automatic color palette design using color image and sentiment analysis. 於 2019 IEEE 4th International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2019 (頁 389-392). [8725717] (2019 IEEE 4th International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCCBDA.2019.8725717