  We introduce a method to perform automatic thresholding of SIFT descriptors
that improves matching performance by at least 15.9% on the Oxford image
matching benchmark. The method uses a contrario methodology to determine a
unique bin magnitude threshold. This is done by building a generative uniform
background model for descriptors and determining when bin magnitudes have
reached a sufficient level. The presented method, called meaningful clamping,
contrasts from the current SIFT implementation by efficiently computing a
clamping threshold that is unique for every descriptor.
