Nister, D., Stewenius, H.: Scalable recognition with a vocabulary tree. Schindler, G., Brown, M., Szeliski, R.: City-scale location recognition. Jacobs, N., Satkin, S., Roman, N., Speyer, R., Pless, R.: Geolocating static cameras.
Snavely, N., Seitz, S.M., Szeliski, R.: Photo tourism: exploring photo collections in 3d. The proposed image group localization method can deal with very unclear queries which are not capable of being geolocated individually.Īgarwal, S., Snavely, N., Simon, I., Seitz, S.M., Szeliski, R.: Building rome in a day. The final location is determined based on the Confidence of Localization parameter. First, each image is localized individually then, the rest of the images in the group are matched against images in the neighboring area of the found first match. In addition, we propose a novel approach to localize groups of images accurately in a hierarchical manner. A parameter called Confidence of Localization which is based on the Kurtosis of the distribution of votes is defined to determine how reliable the localization of a particular image is. Then, a smoothing step with an associated voting scheme is utilized this allows each query descriptor to vote for the location its nearest neighbor belongs to, in order to accurately localize the query image. A novel GPS-tag-based pruning method removes the less reliable descriptors.
In order to localize a query image, the tree is queried using the detected SIFT descriptors in the query image. We propose a localization method in which the SIFT descriptors of the detected SIFT interest points in the reference images are indexed using a tree.
In this paper, we address the problem of finding the GPS location of images with an accuracy which is comparable to hand-held GPS devices.We leverage a structured data set of about 100,000 images build from Google Maps Street View as the reference images. Finding an image’s exact GPS location is a challenging computer vision problem that has many real-world applications.
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