number of matches leads to wrong poses with many inliers. In the second part of the talk, we thus consider the problem of finding a better decision criterion than the raw inlier count. We show that geometric bursts, i.e., spatial configurations appearing at multiple places in a scene, are a major reason why the raw inlier count fails for large-scale location recognition. We introduce simple schemes that allow us to efficiently detect geometric bursts during query time. We show experimentally that down-weighting inliers based on the number of bursts they appear in allows us to better decide between correct and incorrect place recognition results and significantly boosts the location recognition performance.