Automatic Thresholding of SIFT Descriptors

Matthew R. Kirchner

doi:10.1109/icip.2016.7532365

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arXiv · 1811.03173

Automatic Thresholding of SIFT Descriptors

Matthew R. Kirchner

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Abstract

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.

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BibTeX RIS

Matthew R. Kirchner. 2018-11-07. Automatic Thresholding of SIFT Descriptors. https://doi.org/10.1109/icip.2016.7532365

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