Rotation-invariant fast features for large-scale recognition and real-time tracking

We present an end-to-end feature description pipeline which uses a novel interest point detector and rotation-invariant fast feature (RIFF) descriptors. The proposed RIFF algorithm is 15x faster than SURF [1] while producing large-scale retrieval results that are comparable to SIFT [2]. Such high-speed features benefit a range of applications from mobile augmented reality (MAR) to web-scale image retrieval and analysis. In particular, RIFF enables unified tracking and recognition for MAR.