Preamble

Journal of Zhejiang University (Engineering Science)

Knowledge Graph for the Intelligent Generation of Artistic Image Creation: Constructing a New Annotation System
School of Software Technology, Zhejiang University; College of Art and Archaeology, Zhejiang University

3. 浙江大学

Abstract

College of Computer Science and Technology, Hangzhou CreateCloud Technology Co., Ltd.

This research aims to establish a unified, systematic, and referable knowledge framework for the annotation of art image datasets, addressing the issues of ambiguous definitions and inconsistent results caused by the lack of common standards during the annotation process. To achieve this goal, the study builds upon the fundamental principles of art image composition and integrates the "Structured Theory of Visual Knowledge" proposed in On Visual Knowledge. This theory posits that visual knowledge must achieve precise expression of spatial shapes and dynamic relationships through "typical categories" and "hierarchical structures." Furthermore, this work resonates with the "Dual Attributes of Cultural Elements" theory from Cultural Composition.

By conducting a deep synthesis of Western and Chinese art theories and innovatively incorporating a Chinese cultural perspective, we have constructed a hierarchical and systematic knowledge graph for art images. This graph deconstructs the core visual language of art images and integrates the unique spatial theories and symbolic systems of Chinese painting as a reference and supplement to Western art theory. By transforming qualitative artistic concepts into a clear, structured framework, this model aligns with the cognitive principle that "visual knowledge precedes verbal knowledge." Consequently, it provides an interpretable and reason-able foundation of visual knowledge for generative AI and cross-cultural art analysis. This framework ensures high quality and consistency in annotated data, providing critical support for artistic intelligence research in the AI 2.0 era.

关键词

Knowledge Graph for Intelligent Generation of Artistic Image Creation: Constructing a New Annotation Hierarchy

Abstract: Artistic images; Knowledge Graph; Chinese cultural perspective; Painting language; Structured framework.

1. Introduction

The rapid development of machine learning and deep learning has revolutionized the field of intelligent image generation. However, bridge the gap between computational algorithms and the nuanced understanding of artistic creation remains a significant challenge. This paper proposes a novel knowledge graph framework designed specifically for the intelligent generation of artistic images, with a particular emphasis on the Chinese cultural perspective and the unique formal language of painting. By constructing a new annotation hierarchy, we aim to provide a structured framework that captures both the technical attributes and the profound cultural connotations inherent in artistic works.

2. Theoretical Framework and Annotation Hierarchy

The core of our approach lies in the integration of traditional artistic theory with modern data structures. Unlike standard image datasets that focus on object recognition, our framework prioritizes the "painting language"—the specific techniques, brushwork, and compositional rules that define an artist's style.

2.1 The Chinese Cultural Perspective

Incorporating a Chinese cultural perspective is essential for a comprehensive understanding of Eastern aesthetics. This involves mapping concepts such as Shanshui (landscape) principles, the use of "blank space" (Liu Bai), and the symbolic meanings associated with specific motifs. By embedding these cultural nuances into the knowledge graph, we enable generative models to produce works that are not only visually coherent but also culturally authentic.

2.2 Structured Framework for Painting Language

To facilitate machine understanding, we decompose the artistic process into a structured hierarchy. This hierarchy includes:

• Technical Layer: Details regarding medium, substrate, and primary brush techniques.
• Formal Layer: Analysis of composition, color theory, and structural balance.
• Semantic Layer: The thematic content and historical context of the artwork.
• Aesthetic Layer: Higher-level emotional qualities and stylistic movements.

[FIGURE:1]

3. Knowledge Graph Construction

The construction of the knowledge graph involves the systematic extraction of entities and relations from a curated corpus of art historical texts and high-resolution image metadata. We utilize a multi-modal approach to ensure that visual features are aligned with linguistic descriptions.

3.1 Data Annotation and Refinement

The annotation process follows the newly defined hierarchy. Each artistic image is treated as a node connected to various attributes through defined relationships. For example, an image node might

College of Art and Archaeology, Zhejiang University, Hangzhou 310028,China ; 3. College of Computer Science and Technology, Zhejiang Uni versity, Hangzhou 310028,China Hangzhou Zaowu Cloud Technology Co., Ltd.

Hangzhou 310028,China

Abstract

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 resu lts 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 Pan Yun he 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.

This research stands at the intersection of two major contemporary trends: on one hand, artificial intelligence is advancing toward a stage characterized by data-driven visual and multimodal intelligence; on the other hand, cultural excavation and preservation have become vital for strengthening cultural confidence and promoting the inheritance of civilizations. However, in the specific domain of artistic creation, current artificial intelligence—represented primarily by deep learning—still faces fundamental limitations.

While neural networks have achieved significant progress in tasks such as artwork style classification and authorship identification, they remain essentially "black box" systems based on statistical pattern matching.

These models can identify that a painting belongs to the Baroque style, for instance, but they are unable to explain the underlying reasons. They fail to understand that a specific style emerges from the internal combinatorial logic of visual language elements—such as composition, lighting, color, and brushwork—and they lack insight into the cultural concepts and aesthetic paradigms deeply embedded behind these visual choices. This phenomenon represents the "semantic gap" in the field of computational art that urgently needs to be bridged. To address this challenge, this study constructs the Painting Language Knowledge Graph (PLKG). By deconstructing the knowledge ontology of artistic interpretation and aesthetic concepts into a representational framework, we provide an effective system for artistic expression. The significance of this approach lies in its ability to treat visual paradigms and aesthetic theories with equal importance, thereby constructing a comprehensive framework for cultural digitalization and heritage preservation.

1 理论基础：从文化缺省到视觉知识

Design and Artificial Intelligence; Cultural Constitution Theory. Key words: Knowledge Graph; Chinese Cultural Painting; Language Structured Framework.

This research addresses the modern "Cultural Default" identified by Professor Pan Yunhe, which pertains to the rationality and industrial application of design. However, when this default occurs, the internal essence of the culture can be compromised. The root of this phenomenon lies in the need for a "Cultural Constitution" based on "Cultural Elements." We define Cultural Elements as the fundamental units of a culture that possess distinct characteristics. By defining a design methodology centered on these elements, we can utilize them to construct a framework for Visual Knowledge. Professor Pan Yunhe has pointed out that human visual knowledge (such as semantic networks) is essential for design; specifically, design is the reconstruction of these structures and visual knowledge. Concepts serve as the basic "Prototypes" and "Categories" of visual knowledge, functioning to enhance qualitative stability and modeling. In the context of artistic terminology, these concepts manifest as a system of relationships. For example, visual knowledge reasoning involves analyzing the constituent elements and their dynamic trends.

These features are presented through spatial relationships such as foreground and background. The construction is not merely about the dynamics of brushstrokes, but rather integrates the "Cultural Constitution" theory from the design field with the "Visual Knowledge" found in artificial intelligence technology.

2 PL

A structured, machine-readable art knowledge graph serves as a foundational tool for artists to organize artistic knowledge. This study systematically deconstructs the composition, brushwork, and systemic utility of such graphs. This multi-dimensional, compositional analysis allows artists to identify visual elements within a specific set of choices—elements that serve as critical inputs for artificial intelligence. Through the systematic research of various visual styles, such as those by Mattaro and Kaidong, we demonstrate the robust explanatory power of this classification method within the field of Western art.

These elements serve as the visual foundation of the image, allowing the composition to present a structured form. This is particularly evident in the background, which, together with the subject, constitutes a cohesive whole. In the field of Chinese art, specifically regarding spatial depth, the "foreground" of the work creates a distinct visual presence, illustrating the relationship between humanity and nature.

2.2 艺术图

Aesthetic Deconstruction and Representation in Computational Vision

The classification of "deconstruction" within aesthetics represents a creative methodology where the artist's interpretation of nature corresponds to expressive representation. In the field of computational vision, this approach manifests through the artist's ability to manipulate formal elements. Regarding expressive representation, the concept of "modeling" and "brushwork" allows for the depiction of an object's essence, which ultimately constitutes the formal integrity of the visual composition.

To provide a unified framework for these two perspective systems, scholars have systematically categorized them based on mathematical computation and specific viewpoints. This approach defines the two systems distinctly while highlighting their integration in contemporary art.

[TABLE:1]

Tab.1 Comparison of Perspective Systems

Feature Western Linear Perspective Chinese Conceptual Perspective Foundations Euclidean geometry and optics Taoist philosophy and subjective observation Viewpoint Fixed, single viewpoint ("the window") Moving, multiple viewpoints ("scattered perspective," "changing scenery with every step") Objective Creating rational, measurable space Evoking immersive, experiential, and emotional space ("Yijing") Temporal Logic Capturing a single, frozen moment ("snapshot") Unfolding a continuous space-time narrative (especially in long scrolls) Techniques Vanishing point, horizon line The "Three Distances" (High, Deep, and Level Distance)

In Western linear perspective, light is treated as a physical phenomenon governed by rigorous logic. This logic emphasizes a strong, singular light source. In contrast, Chinese conceptual perspective does not rely on a fixed light source; instead, it is driven by perception and cultural symbolism. The choice of color in Chinese painting is similarly dictated by these principles, where the symbolic nature of color transcends mere optical realism.

The sfumato technique and the use of square-brush strokes represent more than just a physical application of paint. While the artist utilizes these specific brushwork styles to define form and texture, the process is fundamentally an expression of the artist's internal creative vision. These techniques are not merely mechanical skills; they are deeply embedded in the artist's personal style and serve as a primary vehicle for conveying their unique artistic identity.

The innovation of this approach lies in its fundamental shift in conceptual philosophy. It no longer treats the aesthetic object as a static, independent entity; instead, it adapts and reconfigures the object according to specific requirements. The underlying logic is conceptual in nature. These two distinct philosophies serve as the foundation for the creation and interpretation of cultural and artistic works.

3 PL

Composition, brushwork, and artistic theory are rooted in the selection and artistic "composition types."

The classification originates from the quantification of artistic principles. For instance, this research adopts a modern visual perspective on visual dynamics, categorizing composition based on its relationship to artistic theory. Following the frameworks of Hattaro and Kaidong, we distinguish between traditional and modern artistic compositions. The importance of composition is highlighted in \cite{01234567}, which identifies unique composition types such as "Full-frame Composition," "Symmetric Composition," "Point Composition," and "Line Composition." The former two focus on "pictorial layout," while the latter two focus on "viewpoint," embodying the aesthetic concepts of abstraction.

Point composition is further divided into "Visual Focus" and "Visual Dynamics." Following the methodology of Hattaro and Kaidong, composition is categorized into several distinct types, namely:

Visual weight, such as intensity, saliency, and visual layout, along with visual @ABCD, represents the temporal dimension of composition and the rationality of its intensity. Unlike "color," which is frequently used in artwork style research, this study focuses on artistic imagery driven by style and brushwork, specifically looking at how brushwork and the artist contribute to stylistic construction. This knowledge graph constructs a dimension where "gestural brushwork" can build form. Together, these elements constitute the knowledge graph, emphasizing the importance of color and brushwork in expressive representation and visual presentation. The artist's approach to nature is a classic manifestation of this concept, where "treating nature" becomes a "formula." This modern artistic definition establishes that the knowledge graph categorizes cylinders and other forms based on modern art principles, which originate from nature and represent its essence.

Its aesthetic source lies in the abstraction of features. This aligns with the organic forms in the knowledge graph, where brushwork represents natural "states" (such as human figures). This logic serves as a means for artists to perform deconstruction and reorganization. This knowledge graph systematizes these processes into directional visual weight and pictorial visual importance, serving as a vital method for reconstructing visual artistic styles and paintings.

The classification is divided into two manifestations. The innovation of this taxonomy lies in the systematization of strategies for deconstruction and reorganization. Behind this reorganization is the use of multi-viewpoint MIBNI Figure-Ground relationships. Research into this unique modern art form corresponds to the knowledge graph's focus on the limitations of human perception; since the human eye cannot simultaneously process all details, painters compose by prioritizing elements. In the field of computer vision, neural networks (deep learning) and human visual modeling face challenges regarding scientific validity and rationality. This study addresses these through composition-based methods.

This provides a systematic approach to depth. Through this system, people can achieve decomposition (while color is also systematized). This breaks down elements into their underlying physical causes; that is, science explains this visual presentation through visual perception theory. Human vision has a specific orientation toward pictorial phenomena, and the perceptual system in AI creation facilitates the systematization of how artists handle the relationship between depth and color. On one hand, there is depth; on the other, there is a systematic classification and organization of complex scenes. This method is based on the principle that the visual system processes pictorial elements not merely as techniques, but as the essence of modern painting, which lies in carrying the visual representation of specific cultural concepts. Through this, AI creation becomes systematized.

It is defined as a phenomenon where, when one-point or two-point perspective becomes extreme, the pictorial space undergoes a specific transformation.

Rather than relying solely on rigorous mathematical calculation, this approach considers the artist's dynamic, multi-viewpoint observation within the same space, reflecting the artist's academic background and stylistic choices. It exhibits a significant categorical structure. When applied to modern creation, it can identify how artists integrate these elements. For example, it addresses the cultural limitations of pictorial consistency versus background relationships, providing a basis for the WXORYZ artistic graph. In modern art, based on various cultural concepts, artists often intentionally leave "trends" that challenge conventional perceptions of reality, creating a more intelligent visual grammar.

Natural visual representation is defined as a surface-level manifestation and is regarded as a stylistic technique.

Cognitive limitations serve as the visualization of "cultural genes" for data. This is not merely a physical phenomenon but a multi-dimensional one, which this knowledge graph identifies as the essence of artistic types. This is evidenced by the fact that while some painters focus on form, Baroque painters use points of light to construct the pictorial space. These visual perception theories suggest that the human mind tends to perceptually organize elements, providing a systematic classification for the knowledge graph using modern terms [_B[U]. By analyzing the direction and type of light sources, research shows that a systematic understanding of light—categorized into natural and artificial light—corresponds to different observational types.

The scientific validity of this classification lies in how natural and artificial light serve the artist's goals (e.g., using strong light for emphasis). This system deconstructs the pictorial surface into technical components, defining key parts for learners to understand intuitively. The intensity of light defines the pictorial style. This classification applies modern terminology to the construction of color, which is the most emotional element of the composition, rooted in perception and cultural symbolism.

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Definition: Lightness determines the tonal structure of an image and establishes the fundamental aspects of color intensity, such as

System classification, for

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介绍

"Color" is not merely a physical phenomenon of light; it serves as a profound carrier of cultural meaning. Within various theories of color, it is posited that color possesses the capacity to convey specific symbolic meanings and emotional resonances that are deeply intertwined with particular cultural contexts. For instance, in traditional Chinese culture, the cultural symbolism of color is an indispensable component of artistic analysis. By examining the cultural connotations of color, one can gain deeper insights into the underlying values and aesthetic principles embedded within works of art.

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Artistic Methods of State: Unifying the Visual Field and Material Carriers

The artistic methods employed in representing "state" involve a sophisticated synthesis of conceptual intent and physical execution. Central to this process is the unification of the visual field, where disparate elements are brought into a cohesive aesthetic harmony. By meticulously balancing composition, light, and shadow, the artist creates a singular "state" that transcends the sum of its individual parts. This unification is not merely a formal exercise but a vital step in establishing the emotional and intellectual resonance of the work, ensuring that the viewer's experience is guided by a consistent and intentional atmosphere.

Furthermore, the integration of material carriers plays a critical role in the realization of these artistic states. The choice of medium—whether traditional canvas, industrial materials, or digital interfaces—serves as the physical foundation upon which the artistic vision is built. These material carriers are not passive recipients of form; rather, they interact dynamically with the artist's techniques to influence the final texture, depth, and presence of the piece. By unifying the visual image with its material substrate, the artist bridges the gap between the abstract concept of "state" and its tangible manifestation, allowing the physical properties of the medium to enhance the overall expressive power of the academic or creative inquiry.

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Determining the theory and application of color is essential; during the creative process, one should adhere to these principles. Regarding artificial representation methods, emphasis is placed on brushwork, such as texture and specific color styles.

The visual grammar of art consists of fundamental elements that artists use to systematize their creative domains. Within this framework, this knowledge graph identifies brushwork as the primary means of expressing structure and dynamics. For instance, brushwork defines the composition. These strokes serve to present form, and different techniques can evoke varied emotional responses. Ultimately, brushwork becomes a distinctive technical language with high recognizability; it reflects the artist's unique understanding of light and color, and more importantly, highlights its decisive role as a conscious act of artistic creation.

The evolution of these techniques is deeply rooted in the deepening of painterly concepts. This knowledge graph categorizes the development of surface brushwork and point-based strokes. Building upon this classification, techniques have evolved into sophisticated brushwork styles. In this knowledge graph, these are identified as primary brushwork types, each possessing a unique aesthetic. Texture refers to the tactile visual quality of the painting's surface. Painters utilize these textures to enhance the dimensionality of the canvas and to represent the interplay of light and color.

Texture is manifested through various dimensions such as surface quality, brushwork, and color. At this stage, visual simulation—for example, the fuzzy texture of a cloak—is achieved through these means. There is a noticeable trend toward integrating Western painting brushwork with traditional Chinese painting techniques. One such approach is "incorporating Western methods into Chinese art" Error! Reference source not found.

This research emphasizes the stylistic characteristics of abstract beauty. It focuses on the integration of artistic imagery and the multifaceted driving forces behind its development. Furthermore, it highlights the stylistic features emerging from the evolution of modern art.

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Personal style and artistic technique define the scope of a painter's brushwork language. Different painting tools and surface application methods directly influence the emergence of artistic styles. By constructing a computational art creation system based on the causality of technique—which constitutes the fundamental basis of artistic style—this study conducts a systematic investigation into the technical processes of art. This approach represents a fundamental conceptual shift toward the digitization of techniques. This processing method serves as the primary means for painters to achieve artistic expression. This research systematizes these elements into a coherent framework.

To represent specific textures and qualities, the establishment of a formal system provides a foundation for artistic execution. On one hand, painters utilize these systems to enhance the spatial depth of the composition. Through the management of light and shadow—a method employed since the Renaissance—artists have sought to achieve greater realism. The underlying logic of this approach is rooted in the origins of painting: creating an independent aesthetic object based on observational reality.

In contrast, the artistic logic of the Ming Dynasty was conceptual and knowledge-based. Within the framework of the present knowledge graph, the resulting artworks and artistic styles (such as...

4 结

The "visual knowledge structuring" of art images constructed in this research represents a significant advancement in the field of art. Addressing the current limitations of art images within the framework of artificial intelligence, this paper systematically explores the generation, brushwork, and structural composition of art images. Our research demonstrates that the integration of professional artistic terminology provides a robust foundation for conveying cultural connotations.

In terms of cultural theory, this study transcends the limitations of the "fragmented" nature of traditional art analysis. Proceeding from the systematic logic of Western artistic language, we have constructed a unified analytical framework across multiple dimensions, including texture and brushwork, to address modern art within cultural creation. This approach simultaneously analyzes brushwork while maintaining a holistic perspective.

First, resolving core labels provides feasibility and depth for subsequent analysis. Furthermore, within the realm of artistic intelligence, this serves as a specialized methodology. In the context of style transfer, it incorporates personalized elements and specific stylistic characteristics of artists to realize the inherent logic of artistic creation. Regarding the viewer, art possesses a unique communicative property; as the saying goes, "culture is the foundation, while development lies in innovation." By utilizing cultural elements to overcome cultural limitations and grasp the essence of the medium, we aim to build a "deep artistic semantic" infrastructure. This collaborative construction of a visual knowledge domain allows scholars to further explore the visual dimensions of intangible cultural heritage, thereby advancing the evolution of artistic vision.

参考文献

Visual Knowledge: A New Frontier in Artificial Intelligence

1. Introduction

In the evolution of artificial intelligence, the representation and processing of knowledge have always been core challenges. While traditional AI has achieved significant success in symbolic logic and linguistic processing, the way humans perceive, store, and reason with visual information—what we call "visual knowledge"—remains a critical frontier. This paper explores the theoretical foundations, structural characteristics, and potential applications of visual knowledge within the broader context of machine learning and deep learning.

2. The Concept of Visual Knowledge

Visual knowledge is not merely a collection of images or video frames; rather, it is a structured representation of the physical world that captures spatial relationships, temporal dynamics, and causal structures. Unlike linguistic knowledge, which is often discrete and symbolic, visual knowledge is characterized by its continuous nature and its grounding in the physical properties of the environment.

3. Representation and Learning

The representation of visual knowledge requires a departure from traditional feature extraction. We propose a framework where visual knowledge is organized into hierarchical structures, ranging from low-level geometric primitives to high-level semantic concepts.

3.1 Mathematical Framework

To formalize these representations, we consider the mapping between the visual space and the conceptual space. Let $\mathcal{X}$ represent the input space of visual signals and $\mathcal{K}$ represent the space of visual knowledge. The objective is to learn a transformation $\mathcal{F}: \mathcal{X} \to \mathcal{K}$ such that:

$$ \min_{\theta} \sum_{i=1}^{n} \mathcal{L}(\mathcal{F}(x_i; \theta), k_i) + \lambda \Omega(\theta) $$

where $\mathcal{L}$ is a loss function measuring the discrepancy between the learned representation and the ground truth knowledge $k_i$, and $\Omega(\theta)$ is a regularization term ensuring the stability of the learned parameters $\theta$.

[FIGURE:1]

As shown in [FIGURE:1], the architecture integrates multi-scale feature fusion with a knowledge-driven attention mechanism. This allows the model to focus on salient visual entities while maintaining a global understanding of the scene layout.

4. Reasoning with Visual Knowledge

One of the primary advantages of visual knowledge is its ability to support spatial and physical reasoning. For instance, given a set of objects in a 3D environment, a system equipped with visual knowledge can predict the outcome of

Technology & Electronic Engineering , 2019, 8. 1 - 4

Research on Oil Painting Art Style Classification Based on Multi-Feature Fusion

Abstract: With the rapid development of digital image processing and computer vision technology, the automated classification and recognition of artistic styles have become significant research topics in the field of digital humanities. This paper proposes a method for oil painting art style classification based on multi-feature fusion. By integrating global color features, local texture features, and deep semantic features extracted via convolutional neural networks, we construct a comprehensive representation of artistic styles. Experimental results demonstrate that the proposed multi-feature fusion approach significantly improves classification accuracy compared to single-feature methods, providing a robust technical foundation for the digital management and intelligent retrieval of art collections.

1. Introduction

The classification of oil painting styles is a fundamental task in art history research and museum management. Traditional classification methods rely heavily on the expertise of art historians, which is not only time-consuming and labor-intensive but also subjective. In recent years, machine learning and deep learning have provided new perspectives for the objective analysis of art. However, oil paintings are characterized by complex visual elements, where style is often determined by a combination of color palettes, brushwork textures, and compositional structures. Relying on a single type of feature often fails to capture the nuanced characteristics of different artistic movements.

2. Methodology

2.1 Feature Extraction

To achieve a comprehensive representation of oil painting styles, this study extracts features from three distinct dimensions:

1. Color Features: We utilize color moments and color histograms to represent the global color distribution and tonal characteristics of the paintings.
2. Texture Features: Local Binary Patterns (LBP) and Gabor filters are employed to capture the unique brushwork and surface textures characteristic of different artists and periods.
3. Deep Features: Using pre-trained convolutional neural networks (CNNs), we extract high-level semantic features that represent the structural and compositional elements of the artwork.

2.2 Multi-Feature Fusion

The extracted features are integrated using a fusion strategy to form a high-dimensional feature vector. We explore both early fusion (feature-level) and late fusion (decision-level) techniques to determine the optimal configuration for style discrimination. Let the fused feature vector be represented as $\mathcal{F}$, which combines the weighted components of color, texture, and deep features.

3. Experimental Results and Analysis

The proposed method was evaluated on a large-scale dataset of digitized oil paintings encompassing various styles such

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Exploration of the Structural Language Practice in Modern and Contemporary Architectural Oil Painting

ZHENG Lujia
Shaoxing University of Arts and Sciences, 2024

Abstract

This research explores the evolution and application of structural language within the context of modern and contemporary architectural oil painting. By analyzing the intersection of architectural form and painterly expression, this study investigates how artists utilize geometric abstraction, spatial organization, and material texture to redefine the representation of the built environment. The practice of structural language in oil painting transcends mere mimesis, moving toward a synthesis of subjective emotion and objective architectural logic. Through a systematic review of historical developments and contemporary case studies, this thesis identifies the core mechanisms by which structural elements—such as line, plane, and volume—are manipulated to create a cohesive visual narrative. Furthermore, the study examines the author's own creative practice, detailing the technical and conceptual processes involved in translating architectural structures into the medium of oil painting.

1. Introduction

The relationship between architecture and painting has long been a subject of profound artistic inquiry. In the modern and contemporary eras, this relationship has shifted from traditional perspective-based representation toward a more rigorous exploration of "structural language." Structural language in this context refers to the underlying organizational principles and formal relationships that constitute both the physical architectural subject and the visual composition of the painting itself.

As urban landscapes continue to evolve, artists have increasingly sought to capture the essence of these spaces through a lens that emphasizes construction, fragmentation, and reconstruction. This research aims to clarify how structural language serves as a bridge between the three-dimensional reality of architecture and the two-dimensional surface of the canvas, ultimately fostering a new aesthetic dialogue in contemporary oil painting.

2. The Evolution of Architectural Representation

2.1 From Classical Perspective to Modern Abstraction

Historically, architectural oil painting was dominated by the pursuit of realistic depth and accurate proportions. However, the advent of modernism catalyzed a shift toward the deconstruction of form. Artists began to prioritize the internal logic of the painting over the literal depiction of buildings. This transition marked the beginning of a deliberate structural practice where the "skeleton" of the architecture became the primary vehicle for expression.

2.2 Contemporary Trends and Structural Diversity

In contemporary practice, architectural oil painting has expanded to include diverse approaches, ranging from minimalist geometric abstraction to complex, layered interpretations of urban decay and renewal. The use of structural language has become more fluid, allowing for a synthesis of traditional techniques

方法

The Expression of Love and Death Themes in the Paintings of the Vienna Secession

Abstract

The Vienna Secession, as a significant branch of Art Nouveau, emerged at the turn of the 20th century against a backdrop of profound social transformation and intellectual ferment in Austria. This period was characterized by a dualistic obsession with the vitality of life and the inevitability of decay. Central to the artistic output of this movement was the exploration of "Eros" and "Thanatos"—the themes of love and death. This thesis examines how artists of the Vienna Secession, most notably Gustav Klimt, Egon Schiele, and Oskar Kokoschka, utilized innovative formal languages and symbolic systems to articulate these universal human experiences. By analyzing the socio-cultural influence of Freudian psychoanalysis and the unique aesthetic philosophy of the "Gesamtkunstwerk" (total work of art), this study elucidates how the Secessionists transcended traditional academic boundaries to create a visual dialogue between desire, mortality, and the human psyche.

1. Introduction

At the end of the 19th century, Vienna served as a crucible for modern thought, where the crumbling Austro-Hungarian Empire provided a fertile ground for radical artistic innovation. The Vienna Secession, founded in 1897, sought to break away from the conservative Künstlerhaus and establish a new artistic identity that reflected the complexities of modern life. Among the various subjects explored by these artists, the themes of love and death occupy a preeminent position. These are not merely treated as narrative subjects but are woven into the very fabric of the decorative and expressive techniques employed by the movement.

2. Socio-Cultural Context and Philosophical Foundations

The preoccupation with love and death in Viennese art cannot be separated from the intellectual climate of the era. The rise of psychoanalysis, spearheaded by Sigmund Freud, introduced a new understanding of human drives. Freud’s conceptualization of the life instinct (Eros) and the death drive (Thanatos) found a direct visual parallel in the works of the Secessionists.

Furthermore, the philosophical pessimism of Arthur Schopenhauer and the radical individualism of Friedrich Nietzsche influenced the way artists perceived the cycle of existence. In the "Fin de Siècle" atmosphere, love was often depicted as a fleeting moment of ecstasy shadowed by the looming presence of mortality. This duality created a tension that defined the aesthetic of the period.

3. Gustav Klimt: The Allegory of Life and

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Graphs and Shapes

1. Introduction

In the fields of computer vision and geometric data processing, the relationship between "graphs" (topological structures) and "shapes" (geometric realizations) constitutes a fundamental area of inquiry. While a graph provides the connectivity and relational framework between discrete entities, a shape embeds these entities into a metric space, assigning coordinates and defining the underlying manifold. The synergy between these two representations is critical for tasks ranging from 3D reconstruction to molecular modeling and social network analysis.

2. Theoretical Framework

The mathematical representation of a graph $G = (V, E)$ focuses on the set of vertices $V$ and edges $E$. However, when considering a "shape," we must introduce a mapping $f: V \to \mathbb{R}^d$, which assigns each vertex a position in $d$-dimensional space. This mapping transforms a purely combinatorial object into a geometric one.

2.1 Topological Constraints on Geometry

The topology of a graph inherently constrains the possible shapes it can represent. For instance, the degree distribution of vertices and the presence of specific motifs influence the curvature and local density of the resulting geometric form. In manifold learning, we often assume that the observed data points are sampled from a low-dimensional shape embedded in a high-dimensional space.

TAN Jia xin. Application of Picasso cubism painting language in the oil painting creation of "Tu Yu Xing" [D].

Harbin Normal University, 2024.

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99. DOI:

Study on the Geometric Structure Modeling Language in Oil Painting Creation

Abstract

In the realm of oil painting, the geometric structure modeling language serves as a fundamental framework for visual expression. This research explores how geometric principles inform the composition, spatial depth, and formal integrity of oil paintings. By analyzing the historical evolution of structural modeling—from the foundational perspectives of the Renaissance to the radical deconstruction seen in modernism—this study identifies the core mechanisms through which geometric abstraction and simplification enhance the communicative power of a work. The investigation focuses on the synthesis of mathematical precision and artistic intuition, demonstrating that geometric structures are not merely technical scaffolds but are essential linguistic elements that convey emotional and philosophical depth.

1. Introduction

The evolution of oil painting is inextricably linked to the development of visual languages that define how we perceive and represent reality. Among these, the geometric structure modeling language occupies a central position. It provides the underlying logic that organizes visual elements into a coherent whole, allowing artists to transcend mere imitation of nature. This paper examines the theoretical foundations and practical applications of geometric modeling in oil painting, aiming to clarify its role in both classical and contemporary contexts.

2. Theoretical Foundations of Geometric Structure

Geometric structure in painting refers to the use of basic shapes—such as the circle, square, and triangle—and their three-dimensional counterparts to organize the pictorial space. This language is rooted in the belief that the complex forms of the visible world can be reduced to fundamental geometric solids.

2.1 The Concept of Structural Simplification

At the heart of geometric modeling is the process of simplification. By stripping away superficial details, the artist reveals the "skeleton" of the subject. This structural clarity allows for a more profound exploration of volume and mass. As noted in various historical treatises, the mastery of these primary forms is essential for achieving a sense of permanence and stability in a composition.

2.2 Spatial Organization and Perspective

The application of geometry is most evident in the construction of pictorial space. Through the use of linear perspective and the strategic placement of geometric volumes, artists create the illusion of three-dimensional depth on a two-dimensional surface. This systematic approach to space ensures that every element within the frame maintains a logical relationship to the whole, guided by mathematical proportions and compositional balance.

3. Historical Evolution of the Modeling Language

The use of geometric structures has shifted significantly throughout art history, reflecting broader changes in human perception and philosophical inquiry.

3.1 Classical Foundations

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The aesthetic quality of design materials is not merely an inherent physical property but a complex visual manifestation that bridges the gap between raw substance and artistic intent. In the field of modern design, the selection and application of materials serve as the primary language through which a designer communicates tactile and visual experiences to the audience. As noted in the study of material aesthetics \cite{WANG2006}, the sensory perception of a material—its texture, luster, and weight—constitutes a fundamental component of the overall design narrative.

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The "Line Freehand Brushwork" in Chinese Painting

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Abstract

The art of Chinese painting is fundamentally an art of lines. Among the various techniques employed, "line freehand brushwork" (xiànxièyì) stands as a core aesthetic principle, bridging the gap between formal representation and emotional expression. This paper explores the historical evolution, technical characteristics, and philosophical underpinnings of line-based expression in traditional Chinese art.

1. The Primacy of Line in Chinese Aesthetics

In the tradition of Chinese painting, the line is not merely a boundary used to define shapes; it is an independent expressive element capable of conveying the artist's inner vitality and temperament. Unlike Western classical traditions that often rely on light, shadow, and volumetric modeling, Chinese painting prioritizes the "bone method" of brushwork. The line serves as the structural framework of the image, embodying the rhythmic vitality described in ancient aesthetic treatises.

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2. The Concept of "Freehand" (Xieyi) in Brushwork

The term Xieyi (freehand or "writing the meaning") implies that the brushwork is not intended to be a literal transcription of the physical world. Instead, it is a "writing" process where the artist's subjective intent guides the movement of the ink. In "line freehand brushwork," the quality of the line—its thickness, speed, dryness, and fluidity—becomes a direct reflection of the artist's psychological state.

3. Technical Evolution and Stylistic Variation

The development of line techniques can be traced through various dynasties, from the meticulous "iron-wire" lines of early figure painting to the expressive, spontaneous strokes of the literati painters.

• Linear Precision: Early techniques focused on the descriptive power of the line to capture the essence of the subject.
• Calligraphic Integration: The fusion of calligraphy and painting further enriched the vocabulary of the line, introducing concepts such as "flying white" and varying ink tones to create a sense of space and motion without the need for complex shading.

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4. Philosophical Foundations

The emphasis on line is deeply rooted in Taoist and Confucian philosophies. The "oneness" of the stroke represents the interconnectedness of all things. By mastering the line, the

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From "Rongshi" to "Nixiang": The Rise of Deep Learning and Its Evolution

The field of artificial intelligence has undergone a profound transformation, evolving from early rule-based systems to the sophisticated deep learning architectures that dominate the landscape today. This progression represents more than just a technical shift; it reflects a fundamental change in how we conceptualize machine intelligence—moving from the rigid structures of "Rongshi" (containment/potential) to the dynamic, data-driven paradigms of "Nixiang" (inverse/directional) modeling.

The Paradigm Shift in Neural Information Processing

Recent advancements, as highlighted in Advances in Neural Information Processing Systems 2022, demonstrate that the boundaries of machine learning are being pushed by increasingly complex neural architectures. These systems are no longer limited to simple pattern recognition but are now capable of high-level abstraction and generative reasoning. The transition from traditional statistical methods to deep neural networks has allowed researchers to tackle problems that were previously considered computationally intractable.

[FIGURE:1]

The core of this evolution lies in the optimization of objective functions and the refinement of backpropagation algorithms. By leveraging massive datasets and high-performance computing, deep learning models can now identify latent features within unstructured data. This capability is essential for applications ranging from natural language processing to computer vision, where the underlying distribution of data is often non-linear and high-dimensional.

Mathematical Foundations of Modern Architectures

The mathematical rigor behind these models ensures their stability and convergence. Consider a standard deep neural network where the mapping from input to output is defined by a series of transformations. For a given layer $l$, the activation $a^{(l)}$ is typically expressed as:

$$a^{(l)} = \sigma(W^{(l)}a^{(l-1)} + b^{(l)})$$

where $W^{(l)}$ represents the weight matrix, $b^{(l)}$ is the bias vector, and $\sigma$ denotes a non-linear activation function such as ReLU or a sigmoid. The goal of the learning process is to minimize a loss function $\mathcal{L}(\theta)$, where $\theta = {W, b}$ represents the set of all trainable parameters. In modern contexts, this optimization is often achieved through variants of Stochastic Gradient Descent (SGD), formulated as:

$$\theta_{t+1} = \theta_t - \eta \nabla_{\theta} \mathcal{L}(\theta_t)$$

where $\eta$ is the learning rate.

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The artistic legacies and creative contributions of Lin Fengmian, Zhang Ding, and Wu Guanzhong represent pivotal moments in the evolution of modern Chinese art. Their work, as explored in various scholarly analyses such as those published in 2011 and 2023, reflects a profound synthesis of traditional Chinese aesthetics and Western modernist techniques. Specifically, the discourse surrounding their creative output often draws comparisons to Western masters like Leonardo da Vinci, examining how these artists navigated the intersection of cultural heritage and global artistic trends.

Recent scholarship, including studies from 2016, continues to investigate the formal innovations introduced by these figures. By bridging the gap between ink-wash traditions and contemporary abstraction, Lin Fengmian and his successors redefined the visual language of the 20th century. Their collective influence remains a cornerstone for understanding the trajectory of East Asian art history and its dialogue with international movements.

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Introduction

In the field of medical imaging and diagnostic analysis, the integration of advanced computational techniques has become increasingly vital. Recent developments in machine learning and deep learning have revolutionized how clinical data is interpreted, allowing for more precise identification of pathological features that may be subtle or invisible to the human eye. This study builds upon established methodologies in iconography and image analysis to explore new frontiers in diagnostic accuracy.

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1.1 Background and Motivation

The rapid evolution of digital imaging technology has generated vast amounts of data, necessitating the development of automated systems for efficient processing. Traditional manual analysis is often time-consuming and subject to inter-observer variability. By leveraging sophisticated algorithms, we can standardize the evaluation process, ensuring consistency across different clinical settings. The primary objective of this research is to refine existing image analysis frameworks to better support clinical decision-making.

1.2 Current State of Image Analysis

Current research highlights a significant shift toward quantitative analysis. Rather than relying solely on qualitative descriptions, modern approaches utilize mathematical modeling to characterize tissue properties. For instance, the application of $\mathcal{F}$ transforms and statistical descriptors allows for the extraction of high-dimensional features from standard imaging modalities. These features, often referred to as "radiomics," provide a deeper understanding of the underlying biological processes.

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Methodology

The methodology employed in this study integrates several layers of data processing, from initial image acquisition to final classification. We utilize a robust preprocessing pipeline to normalize intensity values and reduce noise, which is critical for maintaining the integrity of the subsequent feature extraction phase.

2.1 Data Acquisition and Preprocessing

Images were collected from a multi-center database to ensure a diverse representation of pathological cases. Each image underwent a standardized preprocessing routine, including bias field correction and spatial normalization. Let $I(x, y)$ represent the original image intensity at coordinates $(x, y)$. The normalized intensity $\hat{I}(x, y)$ is calculated as:

$$\hat{I}(x, y) = \frac{I(x, y) - \mu}{\sigma}$$

where $\mu$ and $\sigma$ denote the mean and standard deviation of the intensity distribution within the region of interest (ROI).

2.2 Feature Extraction and Selection

Following preprocessing, we extracted a comprehensive set of features, including texture, shape, and intensity-based metrics. To avoid the "curse of dimensionality" and improve model generalizability, a feature

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Form in Painting

The concept of "form" in painting is a fundamental element that transcends mere representation, serving as the structural backbone of visual expression. In the context of contemporary artistic discourse, form is not merely the external boundary of an object but a complex synthesis of line, volume, space, and the artist's subjective perception. As we examine the evolution of form through various historical epochs, it becomes evident that the transition from classical mimesis to modern abstraction reflects a profound shift in how creators engage with reality.

[FIGURE:1]

The construction of form involves a delicate balance between objective observation and aesthetic transformation. In traditional academic painting, form was often synonymous with "correct" proportion and anatomical accuracy, governed by the laws of linear perspective and chiaroscuro. However, the advent of modernism challenged these conventions, allowing form to become an autonomous vehicle for emotional and symbolic meaning. By deconstructing the physical world into its constituent geometric or gestural components, artists have expanded the definition of form to include the invisible forces of rhythm, tension, and balance.

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Furthermore, the relationship between form and space is intrinsic to the painterly process. Form does not exist in a vacuum; it is defined by its interaction with the surrounding negative space and the two-dimensional plane of the canvas. This interaction creates a visual dialogue that guides the viewer's eye and establishes the compositional hierarchy. In contemporary practice, the boundaries of form are increasingly fluid, often merging with color and texture to create a multisensory experience that defies traditional categorization. Ultimately, the mastery of form remains a cornerstone of artistic practice, providing the essential framework through which visual ideas are communicated and preserved.

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Visual Representation in Painting

The visual representation within a painting serves as the primary medium through which artistic intent and technical proficiency are conveyed. In the context of academic analysis, this representation is not merely a reflection of the physical world but a complex synthesis of formal elements—such as line, color, texture, and composition—that work together to construct a cohesive narrative or emotional resonance. By examining these components, one can discern the underlying structural logic that governs the artwork's aesthetic impact.

[FIGURE:1]

The interplay between light and shadow, often referred to as chiaroscuro, plays a critical role in defining volume and spatial depth within the two-dimensional plane. This technique allows the artist to guide the viewer's eye toward specific focal points, creating a hierarchical arrangement of visual information. Furthermore, the choice of palette and the application of brushwork contribute significantly to the "painterly" quality of the work, where the physical traces of the artist's process become an integral part of the final expression.

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When analyzing the representational strategies employed in various artistic movements, it becomes evident that the balance between abstraction and mimesis is constantly shifting. Whether through the meticulous detail of realism or the emotive distortion of expressionism, the visual performance in a painting functions as a sophisticated language. This language facilitates a dialogue between the creator and the observer, transcending linguistic barriers to communicate complex human experiences and conceptual frameworks.

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