Interactive genetic color matching design of cultural and creative products considering color image and visual aesthetics
To optimize the colors used in cultural and creative products, this paper proposes a color matching design method that considers the color image and visual aesthetics. First, 99 color samples are identified based on Chinese traditional colors, and user preferences for 30 image semantic terms are mea...
Ausführliche Beschreibung
Autor*in: |
Li Deng [verfasserIn] Fangyuan Zhou [verfasserIn] Zhirui Zhang [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2022 |
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Schlagwörter: |
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Übergeordnetes Werk: |
In: Heliyon - Elsevier, 2016, 8(2022), 9, Seite e10768- |
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Übergeordnetes Werk: |
volume:8 ; year:2022 ; number:9 ; pages:e10768- |
Links: |
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DOI / URN: |
10.1016/j.heliyon.2022.e10768 |
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Katalog-ID: |
DOAJ008920001 |
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520 | |a To optimize the colors used in cultural and creative products, this paper proposes a color matching design method that considers the color image and visual aesthetics. First, 99 color samples are identified based on Chinese traditional colors, and user preferences for 30 image semantic terms are measured by the semantic differential method. This leads to six color image factors being extracted by factor analysis. Second, quantitative analysis of the color visual aesthetics is applied, and formulas for calculating the harmony, balance, and symmetry are derived. On this basis, an interactive genetic algorithm is developed to promote and optimize the color scheme of cultural and creative products, and a fitness function based on subjective image evaluation and objective visual aesthetics is constructed. The subjective image evaluation adopts interval numbers, and a grayscale approach is used to measure the uncertainty of the subjective evaluation. Through grayscale analysis of the interval fitness values, information reflecting the evolutionary distribution of the population is extracted, before adaptive crossover and mutation probabilities are applied to the evolutionary individuals. Finally, the proposed method is verified through the example of color matching design for a speaker box. The results demonstrate that the proposed approach can effectively assist industrial designers. | ||
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10.1016/j.heliyon.2022.e10768 doi (DE-627)DOAJ008920001 (DE-599)DOAJ544750192e5742d9a61a3101f18e3d56 DE-627 ger DE-627 rakwb eng Q1-390 H1-99 Li Deng verfasserin aut Interactive genetic color matching design of cultural and creative products considering color image and visual aesthetics 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To optimize the colors used in cultural and creative products, this paper proposes a color matching design method that considers the color image and visual aesthetics. First, 99 color samples are identified based on Chinese traditional colors, and user preferences for 30 image semantic terms are measured by the semantic differential method. This leads to six color image factors being extracted by factor analysis. Second, quantitative analysis of the color visual aesthetics is applied, and formulas for calculating the harmony, balance, and symmetry are derived. On this basis, an interactive genetic algorithm is developed to promote and optimize the color scheme of cultural and creative products, and a fitness function based on subjective image evaluation and objective visual aesthetics is constructed. The subjective image evaluation adopts interval numbers, and a grayscale approach is used to measure the uncertainty of the subjective evaluation. Through grayscale analysis of the interval fitness values, information reflecting the evolutionary distribution of the population is extracted, before adaptive crossover and mutation probabilities are applied to the evolutionary individuals. Finally, the proposed method is verified through the example of color matching design for a speaker box. The results demonstrate that the proposed approach can effectively assist industrial designers. Color image Visual aesthetics Interactive genetic algorithm Cultural and creative products Color matching design Science (General) Social sciences (General) Fangyuan Zhou verfasserin aut Zhirui Zhang verfasserin aut In Heliyon Elsevier, 2016 8(2022), 9, Seite e10768- (DE-627)835893197 (DE-600)2835763-2 24058440 nnns volume:8 year:2022 number:9 pages:e10768- https://doi.org/10.1016/j.heliyon.2022.e10768 kostenfrei https://doaj.org/article/544750192e5742d9a61a3101f18e3d56 kostenfrei http://www.sciencedirect.com/science/article/pii/S2405844022020564 kostenfrei https://doaj.org/toc/2405-8440 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 8 2022 9 e10768- |
spelling |
10.1016/j.heliyon.2022.e10768 doi (DE-627)DOAJ008920001 (DE-599)DOAJ544750192e5742d9a61a3101f18e3d56 DE-627 ger DE-627 rakwb eng Q1-390 H1-99 Li Deng verfasserin aut Interactive genetic color matching design of cultural and creative products considering color image and visual aesthetics 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To optimize the colors used in cultural and creative products, this paper proposes a color matching design method that considers the color image and visual aesthetics. First, 99 color samples are identified based on Chinese traditional colors, and user preferences for 30 image semantic terms are measured by the semantic differential method. This leads to six color image factors being extracted by factor analysis. Second, quantitative analysis of the color visual aesthetics is applied, and formulas for calculating the harmony, balance, and symmetry are derived. On this basis, an interactive genetic algorithm is developed to promote and optimize the color scheme of cultural and creative products, and a fitness function based on subjective image evaluation and objective visual aesthetics is constructed. The subjective image evaluation adopts interval numbers, and a grayscale approach is used to measure the uncertainty of the subjective evaluation. Through grayscale analysis of the interval fitness values, information reflecting the evolutionary distribution of the population is extracted, before adaptive crossover and mutation probabilities are applied to the evolutionary individuals. Finally, the proposed method is verified through the example of color matching design for a speaker box. The results demonstrate that the proposed approach can effectively assist industrial designers. Color image Visual aesthetics Interactive genetic algorithm Cultural and creative products Color matching design Science (General) Social sciences (General) Fangyuan Zhou verfasserin aut Zhirui Zhang verfasserin aut In Heliyon Elsevier, 2016 8(2022), 9, Seite e10768- (DE-627)835893197 (DE-600)2835763-2 24058440 nnns volume:8 year:2022 number:9 pages:e10768- https://doi.org/10.1016/j.heliyon.2022.e10768 kostenfrei https://doaj.org/article/544750192e5742d9a61a3101f18e3d56 kostenfrei http://www.sciencedirect.com/science/article/pii/S2405844022020564 kostenfrei https://doaj.org/toc/2405-8440 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 8 2022 9 e10768- |
allfields_unstemmed |
10.1016/j.heliyon.2022.e10768 doi (DE-627)DOAJ008920001 (DE-599)DOAJ544750192e5742d9a61a3101f18e3d56 DE-627 ger DE-627 rakwb eng Q1-390 H1-99 Li Deng verfasserin aut Interactive genetic color matching design of cultural and creative products considering color image and visual aesthetics 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To optimize the colors used in cultural and creative products, this paper proposes a color matching design method that considers the color image and visual aesthetics. First, 99 color samples are identified based on Chinese traditional colors, and user preferences for 30 image semantic terms are measured by the semantic differential method. This leads to six color image factors being extracted by factor analysis. Second, quantitative analysis of the color visual aesthetics is applied, and formulas for calculating the harmony, balance, and symmetry are derived. On this basis, an interactive genetic algorithm is developed to promote and optimize the color scheme of cultural and creative products, and a fitness function based on subjective image evaluation and objective visual aesthetics is constructed. The subjective image evaluation adopts interval numbers, and a grayscale approach is used to measure the uncertainty of the subjective evaluation. Through grayscale analysis of the interval fitness values, information reflecting the evolutionary distribution of the population is extracted, before adaptive crossover and mutation probabilities are applied to the evolutionary individuals. Finally, the proposed method is verified through the example of color matching design for a speaker box. The results demonstrate that the proposed approach can effectively assist industrial designers. Color image Visual aesthetics Interactive genetic algorithm Cultural and creative products Color matching design Science (General) Social sciences (General) Fangyuan Zhou verfasserin aut Zhirui Zhang verfasserin aut In Heliyon Elsevier, 2016 8(2022), 9, Seite e10768- (DE-627)835893197 (DE-600)2835763-2 24058440 nnns volume:8 year:2022 number:9 pages:e10768- https://doi.org/10.1016/j.heliyon.2022.e10768 kostenfrei https://doaj.org/article/544750192e5742d9a61a3101f18e3d56 kostenfrei http://www.sciencedirect.com/science/article/pii/S2405844022020564 kostenfrei https://doaj.org/toc/2405-8440 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 8 2022 9 e10768- |
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10.1016/j.heliyon.2022.e10768 doi (DE-627)DOAJ008920001 (DE-599)DOAJ544750192e5742d9a61a3101f18e3d56 DE-627 ger DE-627 rakwb eng Q1-390 H1-99 Li Deng verfasserin aut Interactive genetic color matching design of cultural and creative products considering color image and visual aesthetics 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To optimize the colors used in cultural and creative products, this paper proposes a color matching design method that considers the color image and visual aesthetics. First, 99 color samples are identified based on Chinese traditional colors, and user preferences for 30 image semantic terms are measured by the semantic differential method. This leads to six color image factors being extracted by factor analysis. Second, quantitative analysis of the color visual aesthetics is applied, and formulas for calculating the harmony, balance, and symmetry are derived. On this basis, an interactive genetic algorithm is developed to promote and optimize the color scheme of cultural and creative products, and a fitness function based on subjective image evaluation and objective visual aesthetics is constructed. The subjective image evaluation adopts interval numbers, and a grayscale approach is used to measure the uncertainty of the subjective evaluation. Through grayscale analysis of the interval fitness values, information reflecting the evolutionary distribution of the population is extracted, before adaptive crossover and mutation probabilities are applied to the evolutionary individuals. Finally, the proposed method is verified through the example of color matching design for a speaker box. The results demonstrate that the proposed approach can effectively assist industrial designers. Color image Visual aesthetics Interactive genetic algorithm Cultural and creative products Color matching design Science (General) Social sciences (General) Fangyuan Zhou verfasserin aut Zhirui Zhang verfasserin aut In Heliyon Elsevier, 2016 8(2022), 9, Seite e10768- (DE-627)835893197 (DE-600)2835763-2 24058440 nnns volume:8 year:2022 number:9 pages:e10768- https://doi.org/10.1016/j.heliyon.2022.e10768 kostenfrei https://doaj.org/article/544750192e5742d9a61a3101f18e3d56 kostenfrei http://www.sciencedirect.com/science/article/pii/S2405844022020564 kostenfrei https://doaj.org/toc/2405-8440 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 8 2022 9 e10768- |
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Interactive genetic color matching design of cultural and creative products considering color image and visual aesthetics |
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To optimize the colors used in cultural and creative products, this paper proposes a color matching design method that considers the color image and visual aesthetics. First, 99 color samples are identified based on Chinese traditional colors, and user preferences for 30 image semantic terms are measured by the semantic differential method. This leads to six color image factors being extracted by factor analysis. Second, quantitative analysis of the color visual aesthetics is applied, and formulas for calculating the harmony, balance, and symmetry are derived. On this basis, an interactive genetic algorithm is developed to promote and optimize the color scheme of cultural and creative products, and a fitness function based on subjective image evaluation and objective visual aesthetics is constructed. The subjective image evaluation adopts interval numbers, and a grayscale approach is used to measure the uncertainty of the subjective evaluation. Through grayscale analysis of the interval fitness values, information reflecting the evolutionary distribution of the population is extracted, before adaptive crossover and mutation probabilities are applied to the evolutionary individuals. Finally, the proposed method is verified through the example of color matching design for a speaker box. The results demonstrate that the proposed approach can effectively assist industrial designers. |
abstractGer |
To optimize the colors used in cultural and creative products, this paper proposes a color matching design method that considers the color image and visual aesthetics. First, 99 color samples are identified based on Chinese traditional colors, and user preferences for 30 image semantic terms are measured by the semantic differential method. This leads to six color image factors being extracted by factor analysis. Second, quantitative analysis of the color visual aesthetics is applied, and formulas for calculating the harmony, balance, and symmetry are derived. On this basis, an interactive genetic algorithm is developed to promote and optimize the color scheme of cultural and creative products, and a fitness function based on subjective image evaluation and objective visual aesthetics is constructed. The subjective image evaluation adopts interval numbers, and a grayscale approach is used to measure the uncertainty of the subjective evaluation. Through grayscale analysis of the interval fitness values, information reflecting the evolutionary distribution of the population is extracted, before adaptive crossover and mutation probabilities are applied to the evolutionary individuals. Finally, the proposed method is verified through the example of color matching design for a speaker box. The results demonstrate that the proposed approach can effectively assist industrial designers. |
abstract_unstemmed |
To optimize the colors used in cultural and creative products, this paper proposes a color matching design method that considers the color image and visual aesthetics. First, 99 color samples are identified based on Chinese traditional colors, and user preferences for 30 image semantic terms are measured by the semantic differential method. This leads to six color image factors being extracted by factor analysis. Second, quantitative analysis of the color visual aesthetics is applied, and formulas for calculating the harmony, balance, and symmetry are derived. On this basis, an interactive genetic algorithm is developed to promote and optimize the color scheme of cultural and creative products, and a fitness function based on subjective image evaluation and objective visual aesthetics is constructed. The subjective image evaluation adopts interval numbers, and a grayscale approach is used to measure the uncertainty of the subjective evaluation. Through grayscale analysis of the interval fitness values, information reflecting the evolutionary distribution of the population is extracted, before adaptive crossover and mutation probabilities are applied to the evolutionary individuals. Finally, the proposed method is verified through the example of color matching design for a speaker box. The results demonstrate that the proposed approach can effectively assist industrial designers. |
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Interactive genetic color matching design of cultural and creative products considering color image and visual aesthetics |
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|
score |
7.4000654 |