BUDA.ART: A Multimodal Content-based Analysis And Retrieval System For Buddha Statues
2019 · Benjamin Renoust, Matheus Oliveira Franca, Jacob Chan, et al.
Abstract
We introduce BUDA.ART, a system designed to assist researchers in Art History, to explore and analyze an archive of pictures of Buddha statues. The system combines different CBIR and classical retrieval techniques to assemble 2D pictures, 3D statue scans and meta-data, that is focused on the Buddha facial characteristics. We build the system from an archive of 50,000 Buddhism pictures, identify unique Buddha statues, extract contextual information, and provide specific facial embedding to first index the archive. The system allows for mobile, on-site search, and to explore similarities of statues in the archive. In addition, we provide search visualization and 3D analysis of the statues
Authors
(none)
Tags
Stats
Related papers
- Iart: A Search Engine For Art-historical Images To Support Research In The Humanities (2021)8.35
- Artseek: Deep Artwork Understanding Via Multimodal In-context Reasoning And Late Interaction Retrieval (2025)2.16
- How To Read Paintings: Semantic Art Understanding With Multi-modal Retrieval (2018)13.93
- Linking Art Through Human Poses (2019)10.74
- Object Retrieval And Localization In Large Art Collections Using Deep Multi-style Feature Fusion And Iterative Voting (2021)7.50
- Stylebabel: Artistic Style Tagging And Captioning (2022)7.81
- A Sketch+text Composed Image Retrieval Dataset For Thangka (2026)0.78
- Visual Link Retrieval And Knowledge Discovery In Painting Datasets (2020)12.25