ML Anthology

Epitomic Location Recognition

Kai Ni, Anitha Kannan, Antonio Criminisi, John M. Winn
CVPR 2008
doi:10.1109/CVPR.2008.4587585 /cvpr/2008/ni2008cvpr-epitomic/

Abstract

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.

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Cite

Text

Ni et al. "Epitomic Location Recognition." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2008. doi:10.1109/CVPR.2008.4587585

Markdown

[Ni et al. "Epitomic Location Recognition." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2008.](https://mlanthology.org/cvpr/2008/ni2008cvpr-epitomic/) doi:10.1109/CVPR.2008.4587585

BibTeX

@inproceedings{ni2008cvpr-epitomic,
  title     = {{Epitomic Location Recognition}},
  author    = {Ni, Kai and Kannan, Anitha and Criminisi, Antonio and Winn, John M.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2008},
  doi       = {10.1109/CVPR.2008.4587585},
  url       = {https://mlanthology.org/cvpr/2008/ni2008cvpr-epitomic/}
}