Buddha Face and AI
In collaboration with Prof. Fujioka with Graduate School of Letters/School of Letters, Osaka University, we are attempting to create an AI for analyzing various aspects of Buddha faces in images.
Focusing on the face of the Buddha image, i.e., “Buddha face”, we analyze the characteristics of the style of each region, era, and author using statistical and machine learning approaches based on images and 3D geometric data, building a genealogy of Buddha faces. This is to realize style judgment based on the knowledge obtained from data, not based on the experience of art historians, which promotes the globalization of the Buddha statue research and also helps to identify the genealogy of Buddha faces propagated through the Silk Road, giving a new perspective on the spread of culture in Asia.
We have built several interfaces to browse through a large corpus of precious Buddha faces for facilitating annotations on the basic meta-data on the statues, which will then serve as a source to train more sophisticated models for analyzing them.
For example, we built a model that can embed various information on target entities (i.e., Buddha status), such as authors, eras, places, etc., into a vector representation of images and use them for other tasks like classification, through the model below.
- Context-aware embeddings for automatic art analysis
- ContextNet: representation and exploration for painting classification and retrieval in context
- Buda.art: A multimodal content-based analysis and retrieval system for Buddha statues
- GCNBoost: Artwork Classificationby Label Propagation Through a Knowledge Graph
- WRIME: A new dataset for emotional intensity estimation with subjective and objective annotations