Summary of Artificial Intelligence For Geometry-based Feature Extraction, Analysis and Synthesis in Artistic Images: a Survey, by Mridula Vijendran et al.
Artificial Intelligence for Geometry-Based Feature Extraction, Analysis and Synthesis in Artistic Images: A Survey
by Mridula Vijendran, Jingjing Deng, Shuang Chen, Edmond S. L. Ho, Hubert P. H. Shum
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores how artificial intelligence (AI) can be used to analyze, identify, and generate digitized artistic images, revolutionizing the visual art industry. By integrating geometric data into AI models, researchers address challenges such as high inter-class variations, domain gaps, and separating style from content. The study reveals that incorporating geometric information not only improves AI-generated graphics synthesis quality but also enables effective distinction between style and content. Methods like extracting geometric data from artistic images are discussed, as well as the impact on human perception and its use in discriminative tasks. Additionally, innovative annotation techniques for improving data quality and using geometric data to enhance model adaptability and output refinement are explored. Overall, incorporating geometric guidance boosts model performance in classification and synthesis tasks, providing crucial insights for future AI applications in the visual arts domain. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how artificial intelligence can be used to make art better. It looks at how combining AI with geometric data (like shapes and patterns) helps computers generate more realistic and beautiful images. The researchers found that this combination improves not just how good the computer-generated art is, but also how well it can tell apart different styles and meanings in art. They also share some ways to make the data used for training AI models better, like using new annotation techniques. Overall, this study shows that combining geometric data with AI has big potential for improving computer-generated art and making it more useful in the future. |
Keywords
» Artificial intelligence » Classification
