Department of Statistics, UCLA

In this work we construct scale invariant descriptors (SIDs) without requiring the estimation of image scale; we thereby avoid scale selection which is often unreliable.

Our starting point is a combination of Log-Polar sampling and spatially-varying smoothing that converts image scalings and rotations into translations. Scale invariance can then be guaranteed by estimating the Fourier Transform Modulus (FTM) of the formed signal as the FTM is translation invariant. We build our descriptors using phase, orientation and amplitude features that compactly capture the local image structure. Our results show that the constructed SIDs outperform state-of-the-art descriptors on standard datasets.

A main advantage of SIDs is that they are applicable to a broader range of image structures, such as edges, for which scale selection is unreliable. We demonstrate this by combining SIDs with contour segments and show that the performance of a boundary-based model is systematically improved on an object detection task.