I wish to capture the present with the eye of the past. — Patrick Summerfield
This paper presents a novel method for location recognition, which exploits an epitomic representation to achieve both high efficiency and good generalization.
A generative model based on epitomic image analysis captures the appearance and geometric structure of an environment while allowing for variations due to motion, occlusions and non-Lambertian effects. The ability to model translation and scale invariance together with the fusion of diverse visual features yield enhanced generalization with economical training.
Experiments on both existing and new labelled image databases result in recognition accuracy superior to state of the art with real-time computational performance.
Epitomic Location Recognition, Kai Ni, Anitha Kannan, Antonio Criminisi, and John Winn, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (ICCV), 2008
Epitomic Location Recognition, Kai Ni, Anitha Kannan, Antonio Criminisi, and John Winn, IEEE Transactions on Pattern Analysis & Machine Intelligence (PAMI), 2009
The CVPR paper won the award of the Best Student Paper Runner-Up, and its oral presentation can be downloaded here. The movie can be viewed here.