In Defense of the Eight-Point Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Inverted files for text search engines
ACM Computing Surveys (CSUR)
World-scale mining of objects and events from community photo collections
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Modeling the World from Internet Photo Collections
International Journal of Computer Vision
Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Toward Fully Automatic Geo-Location and Geo-Orientation of Static Outdoor Cameras
WACV '08 Proceedings of the 2008 IEEE Workshop on Applications of Computer Vision
Improving Bag-of-Features for Large Scale Image Search
International Journal of Computer Vision
Avoiding confusing features in place recognition
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Efficient relative camera orientation detection for mobile applications
Proceedings of the 1st international workshop on Mobile location-based service
Total recall II: Query expansion revisited
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Three things everyone should know to improve object retrieval
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Find where you are: a new try in place recognition
The Visual Computer: International Journal of Computer Graphics
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There are millions of mobile phone applications based on location. Using a photo to precisely locate users location is useful and necessary. However, real-time location recognition or retrieval system is a challenging problem due to the really big differences between the query and the dataset in scale, viewpoint and lighting, or the noise existed in the foreground or background etc. To address this problem, we design a place recognition system and a new famous buildings dataset with ground truth labels. By adding a fast geometric image matching procedure before using RANSAC and applying a relative camera orientation calculation algorithm to filter the dataset collected from the Internet, we can substantially improve the efficiency of spatial verification and recognition accuracy.