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

<p>In this paper, a dataset is obtained by clustering data through a big data clustering algorithm, and a multidimensional state space vector with chaotic trajectories is established by reconstructing the phase space after several iterations to calculate the maximum and minimum similarity distances. The clustering between the localized imagery and narratives of contemporary oil paintings is realized based on the extracted chaotic associative dimensional features. A total of 80 students from an art class at a school were selected to conduct an imagery analysis of the cultural roots of imaginative oil painting narratives, in which the evaluation score of the research class in the imagery of shape and color could reach 10 points. Therefore, the localization of oil painting is a theoretical concept for a Chinese oil painting to go global, which meets the realistic needs and development trend of Chinese oil painting.</p>
</abstract>

In this paper, a dataset is obtained by clustering data through a big data clustering algorithm, and a multidimensional state space vector with chaotic trajectories is established by reconstructing the phase space after several iterations to calculate the maximum and minimum similarity distances. The clustering between the localized imagery and narratives of contemporary oil paintings is realized based on the extracted chaotic associative dimensional features. A total of 80 students from an art class at a school were selected to conduct an imagery analysis of the cultural roots of imaginative oil painting narratives, in which the evaluation score of the research class in the imagery of shape and color could reach 10 points. Therefore, the localization of oil painting is a theoretical concept for a Chinese oil painting to go global, which meets the realistic needs and development trend of Chinese oil painting.

Localized Imagery and Narrative of Chinese Contemporary Oil Painting Based on Big Data

Applied Mathematics and Nonlinear Sciences

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

In this paper, a dataset is obtained by clustering data through a big data clustering algorithm, and a multidimensional state space vector with chaotic...

Localized Imagery and Narrative of Chinese Contemporary Oil Painting Based on Big Data

Online veröffentlicht: 09. Okt. 2023

Seitenbereich: -

Eingereicht: 19. Jan. 2023

Akzeptiert: 19. Apr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00563

Schlüsselwörter
Clustering algorithm, Space vector, Geometric feature volume, Chaotic associative dimension

© 2023 Hui Li, published by Sciendo

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

Localized Imagery and Narrative of Chinese Contemporary Oil Painting Based on Big Data

Online veröffentlicht: 09. Okt. 2023

Seitenbereich: -

Eingereicht: 19. Jan. 2023

Akzeptiert: 19. Apr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00563

SchlüsselwörterClustering algorithm, Space vector, Geometric feature volume, Chaotic associative dimension

© 2023 Hui Li, published by Sciendo

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

Schlüsselwörter
Clustering algorithm, Space vector, Geometric feature volume, Chaotic associative dimension