About ArtAiGo
ArtAiGo is an art search engine designed to help you explore and discover the vast world of art in a more meaningful way. Currently, ArtAiGo has:
- Total Number of Art Images: 54.497
- Total Number of Artists: 6.973
- Total Number of Institutions: 4.190
- Total Number of Crowd-Sourced Tags: 295.343
- Total Number of AI-Sourced Tags: 226.947
- Total Number of Crowd-Sourced Taggings: 5.202.714
- Total Number of AI-Sourced Taggings: 1.452.387
Tags by Language
All tags have been categorized using the Panofsky Method.
Who is Panofsky?
Erwin Panofsky was a highly influential art historian known for developing a systematic approach to analyzing art. His methodology, often referred to as iconology, involves three levels of interpretation:
- Pre-iconographic (273.912): The basic, descriptive level of understanding an artwork, focusing on what is visually apparent—such as shapes, colors, and forms—without any deeper interpretation.
- Iconographic (149.229): This level identifies and interprets the symbols and themes present in the artwork, relating them to specific cultural, religious, or literary references.
- Iconological (89.634): The deepest level of analysis, where the artwork’s broader cultural, historical, and philosophical context is considered, uncovering underlying meanings and messages that may not be immediately visible.
About Me
Hello! I’m Osman, the developer behind ArtAiGo, and someone who dreams of continuing his PhD in Art History.
I’m based in Munich, Germany. I earned my Bachelor’s Degree in Economics from Yildiz Technical University in Istanbul, Turkey, before moving to Munich to pursue a Master’s in Economics at Ludwig Maximilians Universität München (LMU).
After completing my master’s, I was eager to bridge my interest in art with digital humanities. I applied for and was accepted into the PhD program at LMU. My research focused on automating the tagging of art images, building on a large dataset of crowd-sourced tags from the Artigo project, a project led by my professor and the foundation for this current project. You can learn more about the Artigo project here. My goal was not only to generate more tags but also to create more complex, context-aware tagging for art images.
I’m passionate about making art more accessible and engaging. I want to help people see more in art—compare their perspectives with others, connect with the emotions and history embedded in art, and embark on a journey of discovery and reflection. I aim to create an interface that serves this purpose and supports fellow art historians in their research, opening up new opportunities for exploration.
Through this journey, I also became a software developer, driven by the need to bring this vision to life. It took a few years, but I’m thrilled that this web application has finally come to fruition.
This is the first Minimum Viable Product (MVP) I’m releasing, and there’s much more to come. While I’ve run some cleanups and spellchecks over the dataset, much of it still needs further refinement, which I’m actively working on. Future plans include enhancing the search capabilities with AI, adding more high-quality images, and incorporating generative AI features. However, to achieve these ambitious goals, I’ll need some funding—a challenge I’m still figuring out. I would greatly appreciate any support you can offer! Please don’t hesitate to reach out to me at info[at]artaigo.org.