Automatic Thresholding of SIFT Descriptors

11/07/2018

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by   Matthew R. Kirchner, et al.

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We introduce a method to perform automatic thresholding of SIFT descriptors that improves matching performance by at least 15.9 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.