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Feature set reduction for image matching in large scale environments

Image matching in large scale environments is challenging due to the large number of features used in typical representations. In this paper we investigate methods for reducing the number of SIFT (Scale invariant feature transform) features in an image based localization application. We find that reductions of up to 59% in the number of features can result in improved performance of a naive matching algorithm for highly redundant data sets. However, those improvements do not carry over to visual bag of words, where a more moderate feature reduction (up to 16%) is often needed to maintain performance similar to the non-reduced set. Our reduced features have performed better than other robust feature descriptors namely HoG, GIST and ORB on all data sets with naive matching. The main contribution of this paper is the compact feature representation of a large scale environment for robust 2D image matching.