On SIFTs and their scales

Scale invariant feature detectors often find stable scales in only a few image pixels. Consequently, methods for feature matching typically choose one of two extreme options: matching a sparse set of scale invariant features, or dense matching using arbitrary scales. In this paper we turn our attention to the overwhelming majority of pixels, those where stable scales are not found by standard techniques. We ask, is scale-selection necessary for these pixels, when dense, scale-invariant matching is required and if so, how can it be achieved? We make the following contributions: (i) We show that features computed over different scales, even in low-contrast areas, can be different; selecting a single scale, arbitrarily or otherwise, may lead to poor matches when the images have different scales. (ii) We show that representing each pixel as a set of SIFTs, extracted at multiple scales, allows for far better matches than singlescale descriptors, but at a computational price. Finally, (iii...

• Type: Journal
• Year: 2012
• Where: CVPR
• Authors: Tal Hassner, Viki Mayzels, Lihi Zelnik-Manor
• Keywords: Computer Vision, Cvpr 2012, Linear Subspace, Sparse Set, Stable Scales