<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In this paper, by collecting a large number of 3D scenes with traditional cultural styles, after pre-processing, the style analysis task of 3D scenes is realized through two stages of model style element discovery and scene style pattern mining. In the style pattern mining stage, the maximum frequent pattern mining algorithm is used to get the style pattern of the final scene, and the part is visualized. To reduce the user’s selection time, a 3D interior layout recommendation system based on style rule mining is designed using collaborative filtering. Based on the previous work on scene style pattern mining, the style association rules are further mined, and the user is provided with candidate layouts and furniture dynamically and in real-time by introducing user interaction. Comparing the text-improved recommendation algorithm with the traditional recommendation algorithm, the accuracy rate of the traditional algorithm starts to saturate to 65% after TopN>20, while the algorithm in this paper is higher than 80%. In the analysis of traditional cultural contexts of interior art, in terms of the aesthetic acquisition, the highest proportion of aesthetic association is 63.7%. In terms of emotional expression, the proportion of symbolization of emotion is 73.1%. In terms of object comparison, the highest proportion of plurality and flexibility of combinations was 57.5%. Therefore, this paper provides good research ideas for how to better integrate traditional culture into interior design.</p>
</abstract>

In this paper, by collecting a large number of 3D scenes with traditional cultural styles, after pre-processing, the style analysis task of 3D scenes is realized through two stages of model style element discovery and scene style pattern mining. In the style pattern mining stage, the maximum frequent pattern mining algorithm is used to get the style pattern of the final scene, and the part is visualized. To reduce the user’s selection time, a 3D interior layout recommendation system based on style rule mining is designed using collaborative filtering. Based on the previous work on scene style pattern mining, the style association rules are further mined, and the user is provided with candidate layouts and furniture dynamically and in real-time by introducing user interaction. Comparing the text-improved recommendation algorithm with the traditional recommendation algorithm, the accuracy rate of the traditional algorithm starts to saturate to 65% after TopN>20, while the algorithm in this paper is higher than 80%. In the analysis of traditional cultural contexts of interior art, in terms of the aesthetic acquisition, the highest proportion of aesthetic association is 63.7%. In terms of emotional expression, the proportion of symbolization of emotion is 73.1%. In terms of object comparison, the highest proportion of plurality and flexibility of combinations was 57.5%. Therefore, this paper provides good research ideas for how to better integrate traditional culture into interior design.

Analysis of the fusion development of traditional culture concept and interior art design based on 3D scene analysis

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In this paper, by collecting a large number of 3D scenes with traditional cultural styles, after pre-processing, the style analysis task of 3D scenes is...

Analysis of the fusion development of traditional culture concept and interior art design based on 3D scene analysis

Online veröffentlicht: 30. Sept. 2023

Seitenbereich: -

Eingereicht: 18. Dez. 2022

Akzeptiert: 10. Apr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00420

Schlüsselwörter
Interior design, Mining algorithm, Collaborative filtering set, Association rules

© 2023 Shuhua Zhu, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Analysis of the fusion development of traditional culture concept and interior art design based on 3D scene analysis

Online veröffentlicht: 30. Sept. 2023

Seitenbereich: -

Eingereicht: 18. Dez. 2022

Akzeptiert: 10. Apr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00420

SchlüsselwörterInterior design, Mining algorithm, Collaborative filtering set, Association rules

© 2023 Shuhua Zhu, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Interior design, Mining algorithm, Collaborative filtering set, Association rules