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How to extract traditional cultural design elements from a set of images of cultural relics based on F-AHP and entropy

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Abstract

The creative cultural product design, relating to both typical regional cultures and traditional cultural elements, is a hot issue in recent years. However, there is still a lack of systematic and efficient designing methods to guide designing practices. In order to fill this research gap, this paper proposes a new design method based on F-AHP (Fuzzy-Analytic Hierarchy Process) and entropy computation to extract traditional cultural shape design elements from a set of images of cultural relics. Firstly, we collect a set of culture object related to descriptive and adjective words that can express users’ emotional perception and narrow down them into a shortlist via a fitness evaluation process. Secondly, we analyze and extract common shape elements with image processing tools and user choices. Thirdly, we create a full mapping between the shortlisted culture descriptive words and the identified common shape elements and determine the weighting of each shape element against each evaluation indicator through F-AHP. Fourthly, we construct decision-making matrix and extract key shape elements with high information entropy. Finally, we start designing products with extracted cultural elements. A case study of Han Dynasty potter figurines was conducted to verify the feasibility of the proposed approach.

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Acknowledgements

The authors really appreciate the experts, designers and users who offer great help. This study is expected to provide some references to design practices. This research was supported by the “Fundamental Research Funds for the Central Universities” (G2017KY202), National Natural Science Foundation of China (Grant No. 51805043) and the 111 Project (Grant No.B13044).

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Authors and Affiliations

  1. Shaanxi Engineering Laboratory for Industrial Design, Northwestern Polytechnical University, Xi’an, 710072, China

    Yukun Hu, Suihuai Yu, Dengkai Chen & Jianjie Chu

  2. School of Design, Northumbria University, Newcastle upon Tyne, England, NE1 8ST, UK

    Shengfeng Qin

  3. School of Construction Machinery, Chang’an University, Xi’an, 710064, China

    Yanpu Yang

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  1. Yukun Hu
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  2. Suihuai Yu
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  4. Dengkai Chen
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  6. Yanpu Yang
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Correspondence to Dengkai Chen.

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Appendices

Appendix 1

Table 10 Descriptive words
Full size table

Appendix 2

Pair-wise comparison matrix for indicators I1-I5.

$$ {\displaystyle \begin{array}{c}{A}_{11}=\begin{array}{c}{I}_1\\ {}{I}_2\\ {}{I}_3\\ {}{I}_4\\ {}{I}_5\end{array}\overset{I_1\kern0.5em {I}_2\kern0.5em {I}_3\kern0.5em {I}_4\kern0.5em {I}_5}{\left[\begin{array}{ccccc}1& \tilde{9}& \tilde{3}& \tilde{7}& \tilde{5}\\ {}{\tilde{9}}^{-1}& 1& {\tilde{7}}^{-1}& {\tilde{3}}^{-1}& {\tilde{5}}^{-1}\\ {}{\tilde{3}}^{-1}& \tilde{7}& 1& \tilde{4}& \tilde{2}\\ {}{\tilde{7}}^{-1}& \tilde{3}& {\tilde{4}}^{-1}& 1& {\tilde{3}}^{-1}\\ {}{\tilde{5}}^{-1}& \tilde{5}& {\tilde{2}}^{-1}& \tilde{3}& 1\end{array}\right]}{A}_{12}=\begin{array}{c}{I}_1\\ {}{I}_2\\ {}{I}_3\\ {}{I}_4\\ {}{I}_5\end{array}\overset{\begin{array}{ccccc}{I}_1& {I}_2& {I}_3& {I}_4& {I}_5\end{array}}{\left[\begin{array}{ccccc}1& \tilde{8}& \tilde{2}& \tilde{7}& \tilde{4}\\ {}{\tilde{8}}^{-1}& 1& {\tilde{5}}^{-1}& {\tilde{2}}^{-1}& {\tilde{4}}^{-1}\\ {}{\tilde{2}}^{-1}& \tilde{5}& 1& \tilde{4}& \tilde{2}\\ {}{\tilde{7}}^{-1}& \tilde{2}& {\tilde{4}}^{-1}& 1& {\tilde{3}}^{-1}\\ {}{\tilde{4}}^{-1}& \tilde{4}& {\tilde{2}}^{-1}& \tilde{3}& 1\end{array}\right]}{A}_{13}=\begin{array}{c}{I}_1\\ {}{I}_2\\ {}{I}_3\\ {}{I}_4\\ {}{I}_5\end{array}\overset{\begin{array}{ccccc}{I}_1& {I}_2& {I}_3& {I}_4& {I}_5\end{array}}{\left[\begin{array}{ccccc}1& \tilde{7}& \tilde{3}& \tilde{6}& \tilde{5}\\ {}{\tilde{7}}^{-1}& 1& {\tilde{6}}^{-1}& {\tilde{2}}^{-1}& {\tilde{4}}^{-1}\\ {}{\tilde{3}}^{-1}& \tilde{6}& 1& \tilde{4}& \tilde{2}\\ {}{\tilde{6}}^{-1}& \tilde{2}& {\tilde{4}}^{-1}& 1& {\tilde{2}}^{-1}\\ {}{\tilde{5}}^{-1}& \tilde{4}& {\tilde{2}}^{-1}& \tilde{2}& 1\end{array}\right]}\\ {}{A}_{14}=\begin{array}{c}{I}_1\\ {}{I}_2\\ {}{I}_3\\ {}{I}_4\\ {}{I}_5\end{array}\overset{\begin{array}{ccccc}{I}_1& {I}_2& {I}_3& {I}_4& {I}_5\end{array}}{\left[\begin{array}{ccccc}1& \tilde{8}& \tilde{5}& \tilde{7}& \tilde{6}\\ {}{\tilde{8}}^{-1}& 1& {\tilde{6}}^{-1}& {\tilde{2}}^{-1}& {\tilde{4}}^{-1}\\ {}{\tilde{5}}^{-1}& \tilde{6}& 1& \tilde{4}& \tilde{2}\\ {}{\tilde{7}}^{-1}& \tilde{2}& {\tilde{4}}^{-1}& 1& {\tilde{2}}^{-1}\\ {}{\tilde{6}}^{-1}& \tilde{3}& {\tilde{2}}^{-1}& \tilde{2}& 1\end{array}\right]}{A}_{15}=\begin{array}{c}{I}_1\\ {}{I}_2\\ {}{I}_3\\ {}{I}_4\\ {}{I}_5\end{array}\overset{\begin{array}{ccccc}{I}_1& {I}_2& {I}_3& {I}_4& {I}_5\end{array}}{\left[\begin{array}{ccccc}1& \tilde{7}& \tilde{2}& \tilde{5}& \tilde{3}\\ {}{\tilde{7}}^{-1}& 1& {\tilde{6}}^{-1}& {\tilde{4}}^{-1}& {\tilde{5}}^{-1}\\ {}{\tilde{2}}^{-1}& \tilde{6}& 1& \tilde{3}& \tilde{2}\\ {}{\tilde{5}}^{-1}& \tilde{4}& {\tilde{3}}^{-1}& 1& {\tilde{2}}^{-1}\\ {}{\tilde{3}}^{-1}& \tilde{5}& {\tilde{2}}^{-1}& \tilde{2}& 1\end{array}\right]}\end{array}} $$

Appendix 3

Table 11 Crisp value of pair-wise comparison matrixes as to indicators I1-I5
Full size table

Appendix 4

Table 12 Synthetic weights of the pair-wise comparison matrixes of the five users for indicators I1-I5
Full size table

Appendix 5

Evaluation matrix of 10 users for shape elements.

$$ {\displaystyle \begin{array}{c}{A}_{11}=\begin{array}{c}{S}_1\\ {}{S}_2\\ {}{S}_3\\ {}{S}_4\\ {}{S}_5\\ {}{S}_6\\ {}{S}_7\\ {}{S}_8\\ {}{S}_9\\ {}{S}_{10}\end{array}\overset{I_1\kern0.5em {I}_2\kern0.5em {I}_3\kern0.5em {I}_4\kern0.5em {I}_5}{\left[\begin{array}{ccccc}3& 1& 1& 3& 3\\ {}3& 1& 1& 1& 5\\ {}7& 5& 5& 7& 9\\ {}3& 3& 3& 5& 5\\ {}5& 3& 3& 5& 7\\ {}5& 9& 7& 7& 7\\ {}5& 7& 7& 7& 5\\ {}7& 5& 5& 7& 9\\ {}3& 1& 1& 1& 3\\ {}5& 1& 5& 3& 5\end{array}\right]}{A}_{12}=\begin{array}{c}{S}_1\\ {}{S}_2\\ {}{S}_3\\ {}{S}_4\\ {}{S}_5\\ {}{S}_6\\ {}{S}_7\\ {}{S}_8\\ {}{S}_9\\ {}{S}_{10}\end{array}\overset{\begin{array}{ccccc}{I}_1& {I}_2& {I}_3& {I}_4& {I}_5\end{array}}{\left[\begin{array}{ccccc}3& 1& 1& 1& 3\\ {}5& 1& 3& 3& 3\\ {}9& 5& 7& 7& 9\\ {}3& 3& 3& 5& 3\\ {}5& 3& 3& 3& 5\\ {}7& 9& 7& 7& 5\\ {}7& 7& 5& 7& 7\\ {}9& 9& 7& 7& 7\\ {}1& 3& 1& 3& 3\\ {}5& 1& 1& 3& 5\end{array}\right]}{A}_{13}=\begin{array}{c}{S}_1\\ {}{S}_2\\ {}{S}_3\\ {}{S}_4\\ {}{S}_5\\ {}{S}_6\\ {}{S}_7\\ {}{S}_8\\ {}{S}_9\\ {}{S}_{10}\end{array}\overset{\begin{array}{ccccc}{I}_1& {I}_2& {I}_3& {I}_4& {I}_5\end{array}}{\left[\begin{array}{ccccc}1& 3& 1& 3& 3\\ {}3& 3& 1& 3& 3\\ {}7& 5& 7& 7& 9\\ {}3& 1& 3& 3& 3\\ {}5& 3& 3& 3& 5\\ {}7& 9& 7& 7& 7\\ {}7& 9& 7& 5& 7\\ {}7& 7& 7& 7& 5\\ {}5& 1& 3& 1& 1\\ {}3& 1& 3& 1& 5\end{array}\right]}\\ {}{A}_{14}=\begin{array}{c}{S}_1\\ {}{S}_2\\ {}{S}_3\\ {}{S}_4\\ {}{S}_5\\ {}{S}_6\\ {}{S}_7\\ {}{S}_8\\ {}{S}_9\\ {}{S}_{10}\end{array}\overset{\begin{array}{ccccc}{I}_1& {I}_2& {I}_3& {I}_4& {I}_5\end{array}}{\left[\begin{array}{ccccc}3& 1& 3& 3& 3\\ {}3& 1& 1& 1& 5\\ {}7& 5& 7& 7& 9\\ {}3& 5& 1& 3& 3\\ {}5& 3& 3& 3& 5\\ {}7& 9& 7& 7& 7\\ {}7& 7& 7& 7& 5\\ {}7& 7& 5& 7& 7\\ {}5& 1& 3& 1& 1\\ {}5& 1& 5& 1& 5\end{array}\right]}{A}_{15}=\begin{array}{c}{S}_1\\ {}{S}_2\\ {}{S}_3\\ {}{S}_4\\ {}{S}_5\\ {}{S}_6\\ {}{S}_7\\ {}{S}_8\\ {}{S}_9\\ {}{S}_{10}\end{array}\overset{\begin{array}{ccccc}{I}_1& {I}_2& {I}_3& {I}_4& {I}_5\end{array}}{\left[\begin{array}{ccccc}3& 1& 1& 3& 5\\ {}3& 3& 1& 3& 5\\ {}7& 7& 5& 7& 9\\ {}1& 3& 3& 5& 3\\ {}5& 3& 1& 3& 5\\ {}7& 9& 7& 9& 5\\ {}5& 7& 7& 7& 7\\ {}7& 5& 7& 7& 9\\ {}3& 1& 1& 1& 3\\ {}5& 1& 3& 1& 5\end{array}\right]}\end{array}}. $$
$$ {\displaystyle \begin{array}{c}{A}_{16}=\begin{array}{c}{S}_1\\ {}{S}_2\\ {}{S}_3\\ {}{S}_4\\ {}{S}_5\\ {}{S}_6\\ {}{S}_7\\ {}{S}_8\\ {}{S}_9\\ {}{S}_{10}\end{array}\overset{I_1\kern0.5em {I}_2\kern0.5em {I}_3\kern0.5em {I}_4\kern0.5em {I}_5}{\left[\begin{array}{ccccc}5& 1& 1& 1& 3\\ {}5& 1& 1& 3& 5\\ {}7& 5& 7& 7& 9\\ {}5& 3& 5& 1& 5\\ {}5& 3& 1& 1& 5\\ {}7& 9& 9& 7& 7\\ {}7& 7& 5& 7& 7\\ {}9& 7& 7& 9& 9\\ {}3& 1& 1& 1& 1\\ {}3& 3& 5& 3& 3\end{array}\right]}{A}_{17}=\begin{array}{c}{S}_1\\ {}{S}_2\\ {}{S}_3\\ {}{S}_4\\ {}{S}_5\\ {}{S}_6\\ {}{S}_7\\ {}{S}_8\\ {}{S}_9\\ {}{S}_{10}\end{array}\overset{\begin{array}{ccccc}{I}_1& {I}_2& {I}_3& {I}_4& {I}_5\end{array}}{\left[\begin{array}{ccccc}3& 1& 1& 3& 3\\ {}3& 1& 3& 3& 3\\ {}7& 5& 7& 9& 7\\ {}5& 5& 1& 3& 3\\ {}5& 1& 3& 3& 5\\ {}9& 9& 7& 9& 5\\ {}7& 7& 5& 7& 7\\ {}9& 5& 7& 7& 9\\ {}3& 1& 1& 3& 1\\ {}5& 3& 1& 3& 5\end{array}\right]}{A}_{18}=\begin{array}{c}{S}_1\\ {}{S}_2\\ {}{S}_3\\ {}{S}_4\\ {}{S}_5\\ {}{S}_6\\ {}{S}_7\\ {}{S}_8\\ {}{S}_9\\ {}{S}_{10}\end{array}\overset{\begin{array}{ccccc}{I}_1& {I}_2& {I}_3& {I}_4& {I}_5\end{array}}{\left[\begin{array}{ccccc}3& 1& 1& 3& 1\\ {}3& 1& 1& 1& 3\\ {}7& 5& 7& 9& 9\\ {}3& 3& 3& 5& 3\\ {}5& 3& 5& 3& 5\\ {}7& 9& 9& 7& 7\\ {}5& 7& 7& 7& 7\\ {}9& 9& 7& 7& 9\\ {}1& 3& 3& 1& 1\\ {}5& 3& 1& 3& 7\end{array}\right]}\\ {}{A}_{19}=\begin{array}{c}{S}_1\\ {}{S}_2\\ {}{S}_3\\ {}{S}_4\\ {}{S}_5\\ {}{S}_6\\ {}{S}_7\\ {}{S}_8\\ {}{S}_9\\ {}{S}_{10}\end{array}\overset{\begin{array}{ccccc}{I}_1& {I}_2& {I}_3& {I}_4& {I}_5\end{array}}{\left[\begin{array}{ccccc}5& 1& 3& 3& 1\\ {}3& 1& 1& 3& 3\\ {}7& 5& 7& 7& 9\\ {}3& 3& 1& 3& 5\\ {}7& 1& 3& 3& 5\\ {}5& 7& 7& 7& 7\\ {}5& 5& 7& 5& 7\\ {}7& 7& 5& 7& 9\\ {}3& 1& 1& 3& 1\\ {}5& 3& 5& 5& 7\end{array}\right]}{A}_{110}=\begin{array}{c}{S}_1\\ {}{S}_2\\ {}{S}_3\\ {}{S}_4\\ {}{S}_5\\ {}{S}_6\\ {}{S}_7\\ {}{S}_8\\ {}{S}_9\\ {}{S}_{10}\end{array}\overset{\begin{array}{ccccc}{I}_1& {I}_2& {I}_3& {I}_4& {I}_5\end{array}}{\left[\begin{array}{ccccc}3& 3& 1& 1& 1\\ {}3& 3& 1& 1& 5\\ {}7& 5& 5& 7& 7\\ {}3& 5& 3& 5& 5\\ {}7& 3& 3& 7& 5\\ {}5& 7& 9& 7& 7\\ {}5& 7& 7& 7& 7\\ {}7& 5& 5& 5& 7\\ {}3& 1& 1& 1& 1\\ {}5& 3& 3& 3& 1\end{array}\right]}\end{array}} $$

Appendix 6

Synthetic evaluation matrix of 10 typical users

$$ {A}_1={\displaystyle \begin{array}{c}{S}_1\\ {}{S}_2\\ {}{S}_3\\ {}{S}_4\\ {}{S}_5\\ {}{S}_6\\ {}{S}_7\\ {}{S}_8\\ {}{S}_9\\ {}{S}_{10}\end{array}}\overset{I_1\kern0.5em {I}_2\kern0.5em {I}_3\kern0.5em {I}_4\kern0.5em {I}_5}{\left[\begin{array}{ccccc}32& 14& 14& 24& 26\\ {}34& 16& 14& 22& 40\\ {}72& 52& 64& 74& 86\\ {}32& 34& 26& 40& 36\\ {}54& 26& 28& 34& 52\\ {}66& 86& 76& 74& 64\\ {}60& 70& 64& 66& 66\\ {}78& 66& 62& 70& 80\\ {}30& 14& 16& 16& 16\\ {}46& 20& 32& 26& 48\end{array}\right]} $$

Appendix 7

Table 13 Contribution values of 10 shape elements for each image
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Hu, Y., Yu, S., Qin, S. et al. How to extract traditional cultural design elements from a set of images of cultural relics based on F-AHP and entropy. Multimed Tools Appl 80, 5833–5856 (2021). https://doi.org/10.1007/s11042-020-09348-w

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