Abstract: 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
talk 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 will show the following: (i) 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) Representing each pixel as a set
of SIFTs, extracted at multiple scales, allows for far better matches
than single-scale descriptors, but at a computational price. Finally,
(iii) each such set may be accurately represented by a low-dimensional,
linear subspace. A subspace-to-point mapping may further be used to
produce a novel descriptor representation, the Scale-Less SIFT (SLS),
as an alternative to single-scale descriptors. The talk will cover
these contributions, as well as review our related earlier work on
subspace-to-point mappings.
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