Knowledge Graph for Intelligent Generation of Artistic Image Creation: Constructing a New Annotation Hierarchy
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arXiv
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| Format: | Preprint |
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2025
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| author | Kaixin, Jia Kewen, Zhu Huanghuang, Deng Yiwu, Qiu Shiying, Ding Chenyang, Ding Zou, Ning Zejian, Li |
| author_facet | Kaixin, Jia Kewen, Zhu Huanghuang, Deng Yiwu, Qiu Shiying, Ding Chenyang, Ding Zou, Ning Zejian, Li |
| contents | Our study aims to establish a unified, systematic, and referable knowledge framework for the annotation of art image datasets, addressing issues of ambiguous definitions and inconsistent results caused by the lack of common standards during the annotation process. To achieve this goal, a hierarchical and systematic art image knowledge graph was constructed. It was developed based on the composition principles of art images, incorporating the Structured Theory of Visual Knowledge proposed by Academician Yunhe Pan in On Visual Knowledge-which states that visual knowledge must achieve precise expression of spatial forms and dynamic relationships through "prototype-category" and "hierarchical structure". Through in-depth review of Chinese and Western art theories and pioneering integration of the Chinese cultural perspective, this graph took shape. The core visual language of art images was deconstructed by this knowledge graph. Meanwhile, the unique spatial theory and symbolic system of Chinese painting were compared with and supplemented by Western art theories. This graph converts qualitative artistic concepts into a clear structured framework. It not only conforms to the cognitive law that "visual knowledge takes precedence over verbal knowledge" in humans but also provides an interpretable and inferential visual knowledge foundation for AI art generation and cross-cultural art analysis. It ensures the high quality and consistency of annotated data, thus offering key support for art intelligence research in the AI 2.0 era. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_03585 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Knowledge Graph for Intelligent Generation of Artistic Image Creation: Constructing a New Annotation Hierarchy Kaixin, Jia Kewen, Zhu Huanghuang, Deng Yiwu, Qiu Shiying, Ding Chenyang, Ding Zou, Ning Zejian, Li Human-Computer Interaction 68T07 H.5.2; J.5 Our study aims to establish a unified, systematic, and referable knowledge framework for the annotation of art image datasets, addressing issues of ambiguous definitions and inconsistent results caused by the lack of common standards during the annotation process. To achieve this goal, a hierarchical and systematic art image knowledge graph was constructed. It was developed based on the composition principles of art images, incorporating the Structured Theory of Visual Knowledge proposed by Academician Yunhe Pan in On Visual Knowledge-which states that visual knowledge must achieve precise expression of spatial forms and dynamic relationships through "prototype-category" and "hierarchical structure". Through in-depth review of Chinese and Western art theories and pioneering integration of the Chinese cultural perspective, this graph took shape. The core visual language of art images was deconstructed by this knowledge graph. Meanwhile, the unique spatial theory and symbolic system of Chinese painting were compared with and supplemented by Western art theories. This graph converts qualitative artistic concepts into a clear structured framework. It not only conforms to the cognitive law that "visual knowledge takes precedence over verbal knowledge" in humans but also provides an interpretable and inferential visual knowledge foundation for AI art generation and cross-cultural art analysis. It ensures the high quality and consistency of annotated data, thus offering key support for art intelligence research in the AI 2.0 era. |
| title | Knowledge Graph for Intelligent Generation of Artistic Image Creation: Constructing a New Annotation Hierarchy |
| topic | Human-Computer Interaction 68T07 H.5.2; J.5 |
| url | https://arxiv.org/abs/2511.03585 |