Performance evaluation of low-dimensional sifts

Abstract

The scale-invariant feature transform (SIFT) descriptor has been widely applied in many fields due to its resistance to common image transformations. However, the dimension of SIFT is high which makes it not practical in limited-memory systems. Thus, some lower-dimension SIFTs are proposed by using subspace projection techniques. The most popular technique is Principle Component Analysis (PCA) which can produce two different lower-dimension SIFTs, PCA-SIFT and PSIFT. They apply PCA on gradient field of local patches or on a set of training descriptors, respectively. However, the other subspace techniques can be also used. This paper proposes two more low-dimensional SIFTs (namely LPP-SIFT and SPCA-SIFT) by incorporating manifold subspace and sparse eigenspace learning techniques (Locality Preserving Projection and Sparse PCA are used as the exemplary implementations). Although these techniques are not novel, our results demonstrate they can be used to produce low-dimensional SIFTs. More importantly, by comparing their performance to the existing low-dimension SIFTs, we show which of them are more suitable for image matching.