Fakultät für Technische Wissenschaften
Scalable retrieval of high-dimensional feature vectors is an important component of many applications in multimedia and other fields, but also a very challenging problem. In this talk, we discuss the challenges of high-dimensional indexing at scale, and then present two approximate indexing methods designed for large-scale retrieval. We present results from experiments with the two largest feature collections reported in the literature, 28.5 billion SIFT features on a single server and 42.9 billion SIFT features in a distributed setting, and demonstrate an application with interactive retrieval over the 99.2 million images of the YFCC100M collection.
Björn Thór Jónsson, Associate Professor
IT University of Copenhagen, Denmark
Reykjavik University, Iceland
Christian Timmerer (christian [dot] timmerer [at] itec [dot] aau [dot] at)