1. A computer-implemented method for filtering feature point matches for visual object recognition comprising: identifying one or more false positive descriptors of a first training image, wherein each false positive descriptor comprises: a local descriptor having a local match confidence with a different local descriptor of a second training image that exceeds a predetermined threshold, wherein a global image match confidence between the first and the second training images is less than a second threshold; training at least one classifier to discriminate between the one or more false positive descriptors and one or more local descriptors; and filtering one or more feature point matches using the at least one classifier, wherein filtering a feature point match comprises: identifying the feature point match comprising a match between a first local descriptor identified at a first local feature point in a first input image and a second local descriptor identified at a second local feature point in a second input image, determining, from the at least one classifier, that the feature point match exceeds a threshold matching of one or more false positive descriptors, and in response to the determining, filtering the feature point match.