MosAIc: Finding Artistic Connections across Culture with Conditional Image Retrieval
This work addresses the need for cross-cultural artistic exploration and analysis, though it is incremental as it builds on existing image retrieval methods.
The paper tackles the problem of finding semantically related artworks across diverse cultures and media by introducing Conditional Image Retrieval (CIR), which combines visual similarity search with user filters, and demonstrates its application in identifying blind spots in GANs.
We introduce MosAIc, an interactive web app that allows users to find pairs of semantically related artworks that span different cultures, media, and millennia. To create this application, we introduce Conditional Image Retrieval (CIR) which combines visual similarity search with user supplied filters or "conditions". This technique allows one to find pairs of similar images that span distinct subsets of the image corpus. We provide a generic way to adapt existing image retrieval data-structures to this new domain and provide theoretical bounds on our approach's efficiency. To quantify the performance of CIR systems, we introduce new datasets for evaluating CIR methods and show that CIR performs non-parametric style transfer. Finally, we demonstrate that our CIR data-structures can identify "blind spots" in Generative Adversarial Networks (GAN) where they fail to properly model the true data distribution.