Shape quantization and recognition with randomized trees
Neural Computation
Machine Learning
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Keypoint Recognition Using Randomized Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical building recognition
Image and Vision Computing
Modeling and Recognition of Landmark Image Collections Using Iconic Scene Graphs
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Wide area localization on mobile phones
ISMAR '09 Proceedings of the 2009 8th IEEE International Symposium on Mixed and Augmented Reality
Avoiding confusing features in place recognition
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Location recognition using prioritized feature matching
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Accurate image localization based on google maps street view
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
BRIEF: binary robust independent elementary features
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Handling urban location recognition as a 2D homothetic problem
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Geo-localization of street views with aerial image databases
MM '11 Proceedings of the 19th ACM international conference on Multimedia
City-scale landmark identification on mobile devices
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
SmartVisionApp: A framework for computer vision applications on mobile devices
Expert Systems with Applications: An International Journal
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We propose a visual recognition approach aimed at fast recognition of urban landmarks on a GPS-enabled mobile device. While most existing methods offload their computation to a server, the latency of an image upload over a slow network can be a significant bottleneck. In this paper, we investigate a new approach to mobile visual recognition that would involve uploading only GPS coordinates to a server, following which a compact location specific classifier would be downloaded to the client and recognition would be computed completely on the client. To achieve this goal, we have developed an approach based on supervised learning that involves training very compact random forest classifiers based on labeled geo-tagged images. Our approach selectively chooses highly discriminative yet repeatable visual features in the database images during offline processing. Classification is efficient at query time as we first rectify the image based on vanishing points and then use random binary patterns to densely match a small set of downloaded features with min-hashing used to speedup the search. We evaluate our method on two public benchmarks and on two streetside datasets where we outperform standard bag-of-words retrieval as well as direct feature matching approaches, both of which are infeasible for client-side query processing.