Selective attention algorithms produce more interest points than are usable by many computer vision applications. This work suggests a method for ordering and selecting a subset of those interest points, simultaneously increasing the repeatability of that subset. The individual repeatability of a combination of 10
SIFT, Harris-Laplace, and Hessian-Laplace interest points are predicted using generalized linear models (GLMs). The models are produced by studying the 17 attributes of each interest point. Our goal is not to improve any particular algorithm, but to find attributes that affect affine and similarity invariance regardless of algorithm. The techniques explored in this research enable interest point detectors to improve mean repeatability of their algorithm by 4% using a rank-ordering produced by a GLM or by thresholding interest points using a set of five new thresholds. Selecting the top 1% of GLM-ranked Harris-Laplace interest points results in a repeatability improvement of 6%, to 92.4%.
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