Candid Covariance-Free Incremental Principal Component Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Geo-indexed object recognition for mobile vision tasks
Proceedings of the 10th international conference on Human computer interaction with mobile devices and services
Mining GPS traces and visual words for event classification
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Locating key views for image indexing of spaces
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Point matching as a classification problem for fast and robust object pose estimation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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In this paper we propose the construction of a visual content layer which describes the visual appearance of geographic locations in a city. We captured, by means of a Mobile Mapping system, a huge set of georeferenced images (500K) which cover the whole city of Barcelona. For each image, hundreds of region descriptions are computed off-line and described as a hash code. All this information is extracted without an object of reference, which allows to search for any type of objects using their visual appearance. A new Visual Content layer is built over Google Maps, allowing the object recognition information to be organized and fused with other content, like satellite images, street maps, and business locations.