Automatic prediction of interest point stability
Comer, Thomson H.
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Draper, Bruce A. (Bruce Austin), 1962-Many computer vision applications depend on interest point detectors as a primary means of dimensionality reduction. While many experiments have been done measuring the repeatability of selective attention algorithms [MTS+05, BL02, CJ02, MP07, SMBI98], we are not aware of any method for predicting the repeatability of an individual interest point at runtime. In this work, we attempt to predict the individual repeatability of a set of 106 interest points produced by Lowe’s SIFT algorithm [Low03], Mikolajczyk’s Harris-Affine [Mik02], and Mikolajczyk and Schmid’s Hessian-Affine [MS04]. These algorithms were chosen because of their performance and popularity. 17 relevant attributes are recorded at each interest point, including eigenvalues of the second moment matrix, Hessian matrix, and Laplacian-of-Gaussian score. A generalized linear model is used to predict the repeatability of interest points from their attributes. The relationship between interest point attributes proves to be weak, however the repeatability of an individual interest point can to some extent be influenced by attributes. A 4%improvement ofmean interest point repeatability is acquired through two related methods: the addition of five new thresholding decisions and through selecting the N best interest points as predicted by a GLM of the logarithm of all 17 interest points. A similar GLM with a smaller set of author-selected attributes has comparable performance. This research finds that improving interest point repeatability remains a hard problem, with an improvement of over 4% unlikely using the current methods for interest point detection. The lack of clear relationships between interest point attributes and repeatability indicates that there is a hole in selective attention research that may be attributable to scale space implementation.