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© The Author(s) 2012. This article is published with open access at Springerlink.com Abstract Scale-invariant interest points have found several highly successful applications in computer vision, in partic-ular for image-based matching and recognition. This paper presents a theoretical analysis of the scale se-lection properties of a generalized framework for detecting interest points from scale-space features presented in Linde-berg (Int. J. Comput. Vis. 2010, under revision) and com-prising: – an enriched set of differential interest operators at a fixed scale including the Laplacian operator, the determinant of the Hessian, the new Hessian feature strength measures I and II and the rescaled level curve curvature operator, as well as – an enriched set of scale selection mechanisms including scale selection based on local extrema over scale, com-plementary post-smoothing after the computation of non-linear differential invariants and scale selection based on weighted averaging of scale values along feature trajecto-ries over scale. It is shown how the selected scales of different linear and non-linear interest point detectors can be analyzed for Gaus-sian blob models. Specifically it is shown that for a rota-tionally symmetric Gaussian blob model, the scale estimates obtained by weighted scale selection will be similar to the scale estimates obtained from local extrema over scale o