Automatic color palette design using color image and sentiment analysis

Ya Fang Peng, Tzren Ru Chou*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2019 IEEE 4th International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages389-392
Number of pages4
ISBN (Electronic)9781728114095
DOIs
Publication statusPublished - 2019 Apr
Event4th IEEE International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2019 - Chengdu, China
Duration: 2019 Apr 122019 Apr 15

Publication series

Name2019 IEEE 4th International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2019

Conference

Conference4th IEEE International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2019
Country/TerritoryChina
CityChengdu
Period2019/04/122019/04/15

Keywords

  • automatic color palette design
  • color image
  • sentiment analysis
  • word embedding

ASJC Scopus subject areas

  • Software

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