Back to Search Start Over

Image colour application rules of Shanghai style Chinese paintings based on machine learning algorithm.

Authors :
Fu, Rongrong
Li, Jiayi
Yang, Chaoxiang
Li, Junxuan
Yu, Xiaowen
Source :
Engineering Applications of Artificial Intelligence. Jun2024, Vol. 132, pN.PAG-N.PAG. 1p.
Publication Year :
2024

Abstract

Colour is an important factor in the expression of recognizability and cultural identity of regional cultural and creative design. At present, the colour recognition of regional characteristic and the colour association of regional culture mainly rely on the designer's subjective perception. To obtain the target colour resources with reference for regional cultural and design need, this study proposes a scientific method of colour extraction and strong colour association matching of Shanghai style Chinese paintings by machine learning, and applies the related results to the colour design of cultural and creative products. Firstly, using the SLIC superpixel algorithm and Mean shift algorithm to realize the overall dimensionality reduction of the image features and the colour aggregation gradually, so as to extract the characteristic colours of Shanghai style Chinese paintings; Secondly, we introduce a data mining algorithm (Apriori) to mine out the association rules from multiple characteristics colours and filter out strongly associated colour combinations; Finally, we apply the colour combinations and colour tones to the colour design of the creative products. In order to verify the scientificity of the colour extraction and colour matching method proposed in this paper, we selected another painter's paintings in the same school as the algorithm experimental validation sample, similar results were obtained. In addition, we measured user satisfaction using the degree of awakening to Shanghai style culture and the propensity to make consumer decisions as the evaluation dimensions, which proves that the method of this study is effective. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09521976
Volume :
132
Database :
Academic Search Index
Journal :
Engineering Applications of Artificial Intelligence
Publication Type :
Academic Journal
Accession number :
177088647
Full Text :
https://doi.org/10.1016/j.engappai.2024.107903