Learning Local Image Descriptors

Simon Winder and Matthew Brown


In this paper we study interest point descriptors for im-

age matching and 3D reconstruction. We examine the build-

ing blocks of descriptor algorithms and evaluate numerous

combinations of components. Various published descriptors

such as SIFT, GLOH, and Spin Images can be cast into our

framework. For each candidate algorithm we learn good

choices for parameters using a training set consisting of

patches from a multi-image 3D reconstruction where accu-

rate ground-truth matches are known. The best descriptors

were those with log polar histogramming regions and fea-

ture vectors constructed from rectified outputs of steerable

quadrature filters. At a 95% detection rate these gave one

third of the incorrect matches produced by SIFT.


Publication typeInproceedings
Published inIEEE Computer Society Conf. on Computer Vision and Pattern Recognition
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