Images: Large-Scale Vision

The goal of this project is to study computer and human vision when large amounts of visual data become available. We are developing the Scene UNderstanding (SUN) database, a large database of images found on the web organized by scene types that are being fully segmented and annotated. With this large database we are developing computer vision algorithms for scene understanding that make use of a large training combined with non-parametric (memory based) methods. In parallel, we are also studying how humans memorize large amounts of visual information. As a result we try to understand which representations might be useful for developing new efficient computer vision algorithms and also, how can we use computer vision models of human memory to predict which images will be remembered.