Gauge-SURF descriptors

In this paper, we present a novel family of multiscale local feature descrip1 tors, a theoretically and intuitively well justified variant of SURF which is 2 straightforward to implement but which nevertheless is capable of demon3 strably better performance with comparable computational cost. Our family 4 of descriptors, called Gauge-SURF (G-SURF), are based on second-order 5 multiscale gauge derivatives. While the standard derivatives used to build a 6 SURF descriptor are all relative to a single chosen orientation, gauge deriva7 tives are evaluated relative to the gradient direction at every pixel. Like 8 standard SURF descriptors, G-SURF descriptors are fast to compute due to 9 the use of integral images, but have extra matching robustness due to the 10 extra invariance offered by gauge derivatives. We present extensive experi11 mental image matching results on the Mikolajczyk and Schmid dataset which 12 show the clear advantages of our family of descriptors against first-order lo13 cal derivatives based descriptors such as: SURF, Modified-SURF (M-SURF) 14 and SIFT, in both standard and upright forms. In addition, we also show ex15 perimental results on large-scale 3D Structure from Motion (SfM) and visual 16 categorization applications. 17