Global descriptors for images within an image repository accessible to a visual search server are compared based on order statistics processing including sorting (which is a non-linear transform) and heat kernel matching. Affinity scores are computed for Hamming distances between Fisher vector components corresponding to different clusters of global descriptors from a pair of images and normalized to [0, 1], with zero affinity scores assigned to non-active cluster pairs. Linear Discriminant Analysis is employed to determine a sorted vector of affinity scores to obtain a new global descriptor. The resulting global descriptors produce significantly more accurate matching.
VISUAL SEARCH ACCURACY WITH HAMMING DISTANCE ORDER STATISTICS LEARNING