Knowledge Graph for Intelligent Generation of Artistic Image Creation: Constructing a New Annotation Hierarchy
🤖 AI Summary
Art image annotation suffers from ambiguous definitions and inconsistent results due to the absence of a unified standard. Method: This paper constructs the first hierarchical art knowledge graph integrating both Chinese and Western art theories with Academician Yunhe Pan’s structured visual knowledge framework. It innovatively incorporates traditional Chinese painting’s spatial conception and symbolic system, synergizing them with Western formal analysis frameworks, and proposes a “prototype–category + hierarchical structure” dual-dimensional modeling approach for semantic decomposition and structured representation of art concepts. Contribution/Results: The framework significantly improves annotation consistency and interpretability, enabling AI-driven art generation, cross-cultural visual reasoning, and cognitive alignment. It establishes a reusable, extensible knowledge infrastructure for art intelligence research in the AI 2.0 era.
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Establishing a unified knowledge framework for art image annotation
Converting qualitative artistic concepts into structured visual knowledge
Providing interpretable foundations for AI art generation and analysis
Innovation
Methods, ideas, or system contributions that make the work stand out.
Constructing hierarchical art image knowledge graph
Integrating Chinese and Western art theory perspectives
Converting qualitative concepts into structured framework
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