Comprehensive Multi-Attribute Dataset for Aesthetic Assessment of Paintings and Drawings Across 24 Artistic Categories

المفاهيم الأساسية

The authors introduce the first multi-attribute, multi-category dataset specifically tailored for the aesthetic assessment of paintings and drawings, covering 24 distinct artistic categories and 10 aesthetic attributes. This dataset aims to catalyze advancements in the field of computational aesthetics for artistic images.

الملخص

The authors present the Aesthetics of Paintings and Drawings Dataset (APDD), a pioneering multi-attribute, multi-category dataset for the aesthetic assessment of paintings and drawings. The dataset was constructed with the active participation of 28 professional artists worldwide, along with dozens of art students.
APDD is structured into 24 distinct artistic categories based on different painting types, artistic styles, and subject matter. The dataset also includes 10 aesthetic attributes, such as theme and logic, creativity, layout and composition, space and perspective, sense of order, light and shadow, color, detail and texture, overall, and mood. The authors selected specific sets of attributes tailored to each artistic category.
The authors collected 4,985 paintings and drawings from various professional art websites and institutions, maintaining a 3:1 ratio between works by professional artists and student assignments to ensure diversity and representativeness. The images were then annotated by 51 professional annotators, resulting in over 31,100 annotation records.
To assess the aesthetic attributes and total scores of the paintings, the authors propose the Art Assessment Network for Specific Painting Styles (AANSPS), a novel approach designed for the evaluation of aesthetic attributes in mixed-attribute art datasets. The authors compare the performance of AANSPS with other state-of-the-art methods on the APDD dataset, demonstrating its effectiveness in predicting both total aesthetic scores and aesthetic attribute scores.

الإحصائيات

The APDD dataset contains a total of 4,985 images.
The dataset includes over 31,100 annotation records from 51 professional annotators.
Each image in APDD has been evaluated by at least 6 individuals.

اقتباسات

"The construction of a multi-attribute, multi-category dataset in the field of painting aesthetics represents a pioneering new task."
"Through close collaboration with approximately 60 global professional artists and students with high academic qualifications, we have successfully established a clear system for considering the aesthetic components of art images."

الرؤى الأساسية المستخلصة من

Paintings and Drawings Aesthetics Assessment with Rich Attributes for Various Artistic Categories

by Xin Jin,Qian... في arxiv.org 05-07-2024

https://arxiv.org/pdf/2405.02982.pdf

استفسارات أعمق

How can the APDD dataset be leveraged to develop more advanced techniques for the aesthetic assessment of artistic images beyond paintings and drawings?

The APDD dataset, with its multi-attribute, multi-category structure tailored specifically for paintings and drawings, provides a solid foundation for advancing techniques in aesthetic assessment across various artistic mediums. By leveraging the rich attributes and diverse artistic categories present in the dataset, researchers and developers can explore several avenues for enhancing aesthetic assessment beyond traditional paintings and drawings:

Transfer Learning: The attributes and categories defined in the APDD dataset can serve as a transfer learning framework for other artistic mediums. By adapting the scoring criteria and annotation process to different art forms, such as sculptures, installations, or digital art, researchers can apply the principles established in APDD to develop new models for aesthetic assessment.

Expansion to New Mediums: Researchers can use the APDD dataset as a benchmark to collect data and annotations for other artistic mediums not covered in the initial dataset. By expanding the dataset to include photography, digital art, mixed media, or performance art, new models can be trained to assess the aesthetic quality of a broader range of artistic expressions.

Fine-tuning Models: The annotations and scores in the APDD dataset can be used to fine-tune existing aesthetic assessment models or develop new models that incorporate a wider range of attributes and categories. By refining the models based on the nuances of different artistic styles and mediums present in the dataset, more accurate and comprehensive assessments can be achieved.

Cross-Domain Analysis: Researchers can explore the correlations and differences in aesthetic attributes across different artistic mediums by comparing the scores and annotations in the APDD dataset with those from datasets focused on other art forms. This cross-domain analysis can lead to insights on universal aesthetic principles as well as medium-specific characteristics.

Overall, the APDD dataset serves as a valuable resource for developing advanced techniques in aesthetic assessment that transcend traditional boundaries and encompass a diverse array of artistic expressions.

How can the potential limitations of the current aesthetic attribute categorization be addressed and expanded to capture the nuances of different artistic styles and mediums?

While the aesthetic attribute categorization in the APDD dataset provides a comprehensive framework for assessing paintings and drawings, there are potential limitations that can be addressed and expanded upon to capture the nuances of different artistic styles and mediums more effectively:

Incorporating Medium-Specific Attributes: To capture the unique characteristics of different artistic mediums, such as sculpture, installation art, or digital art, new aesthetic attributes tailored to each medium should be identified and included in the categorization. Attributes like texture, materiality, spatial interaction, or interactivity can be crucial for assessing the aesthetic quality of these mediums.

Dynamic Attribute Selection: Implementing a dynamic attribute selection mechanism that adapts to the specific characteristics of each artistic style or medium can enhance the categorization process. By allowing for the inclusion or exclusion of attributes based on the context of the artwork, the categorization can better reflect the nuances of different styles and mediums.

Expert Consultation: Collaborating with a diverse group of experts from various artistic disciplines can provide valuable insights into the specific attributes that are most relevant for assessing different styles and mediums. By incorporating feedback from artists, curators, and art historians, the attribute categorization can be refined to better capture the complexities of artistic expression.

Iterative Refinement: Continuously refining and expanding the attribute categorization through iterative feedback loops and validation processes can help address any limitations and ensure that the categorization remains relevant and comprehensive across a wide range of artistic styles and mediums.

By addressing these potential limitations and actively expanding the attribute categorization to encompass the nuances of different artistic styles and mediums, the aesthetic assessment framework can become more inclusive, nuanced, and adaptable to the diverse landscape of artistic expression.

Given the diverse range of artistic expressions, how can computational aesthetics models be designed to better understand and appreciate the subjective and contextual nature of artistic appreciation?

To design computational aesthetics models that better understand and appreciate the subjective and contextual nature of artistic appreciation across diverse artistic expressions, several key strategies can be implemented:

Contextual Embeddings: Incorporating contextual embeddings that capture the cultural, historical, and thematic context of artworks can enhance the model's ability to interpret and appreciate the nuances of artistic expression. By training the model on a diverse range of contextual data, it can learn to recognize and respond to the cultural significance and historical references embedded in artworks.

Subjective Feedback Mechanisms: Implementing subjective feedback mechanisms that allow users to provide qualitative assessments and personal interpretations of artworks can help the model learn from human perceptions and preferences. By integrating user feedback into the training process, the model can adapt to individual tastes and subjective viewpoints, leading to more personalized aesthetic evaluations.

Multi-Modal Learning: Leveraging multi-modal learning techniques that combine visual, textual, and auditory information can enable the model to analyze artworks from multiple perspectives. By processing information from different modalities, the model can gain a more holistic understanding of artistic expressions and extract nuanced features that contribute to aesthetic appreciation.

Interdisciplinary Collaboration: Fostering interdisciplinary collaboration between computer scientists, artists, art historians, and psychologists can enrich the development of computational aesthetics models. By integrating insights from diverse fields, the models can be designed to encompass a broader spectrum of aesthetic principles and considerations, leading to more comprehensive and nuanced assessments.

Explainable AI: Implementing explainable AI techniques that provide transparency into the model's decision-making process can enhance trust and understanding of the aesthetic assessments. By enabling users to interpret how the model arrives at its evaluations, they can better appreciate the reasoning behind the aesthetic judgments and engage in meaningful dialogues with the system.

By integrating these strategies into the design of computational aesthetics models, researchers can create systems that not only analyze artistic expressions but also understand and appreciate the subjective and contextual aspects of artistic appreciation, leading to more insightful and nuanced aesthetic assessments.