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

Authors:
Gabriel Takacs;Vijay Chandrasekhar;Sam Tsai;David Chen;Radek Grzeszczuk;Bernd Girod
Affiliations:
Microsoft, United States and Stanford University, United States;Stanford University, United States;Stanford University, United States;Stanford University, United States;Microsoft, United States;Stanford University, United States
Venue:
Image Communication
Year:
2013

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Abstract

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.