Large Area Cell Based Image Localization

We present a memory scalable image localization system that uses distributed kd-trees created on overlapping geographic cells using a database of 10 million Google Street View images for an area of approximately 10,000 square kilometers in Taiwan. Given a collection of images over a region of interest (ROI), we generate a database by dynamically creating geographic cells that are optimized so that each cell contains roughly the same number of images. We then create kd-trees for each cell from SIFT features extracted from the images in that cell. When querying the system, we run traditional feature matching on each cell and pool the results for each cell to rerank with a geometric constraint. The key idea is the subdivisions of the ROI into overlapping geographic cells, allowing our system to scale to 10 million images and to efficiently utilize prior query location information when available. We evaluate our system on a test set of 29 geo-tagged images, not from Google Street View, taken throughout Taiwan with various resolutions, aspect ratios, and qualities. We also evaluate our system on a set of 97 images without geo-tag data.