  In computer vision, an entity such as an image or video is often represented
as a set of instance vectors, which can be SIFT, motion, or deep learning
feature vectors extracted from different parts of that entity. Thus, it is
essential to design efficient and effective methods to compare two sets of
instance vectors. Existing methods such as FV, VLAD or Super Vectors have
achieved excellent results. However, this paper shows that these methods are
designed based on a generative perspective, and a discriminative method can be
more effective in categorizing images or videos. The proposed D3
(discriminative distribution distance) method effectively compares two sets as
two distributions, and proposes a directional total variation distance (DTVD)
to measure how separated are they. Furthermore, a robust classifier-based
method is proposed to estimate DTVD robustly. The D3 method is evaluated in
action and image recognition tasks and has achieved excellent accuracy and
speed. D3 also has a synergy with FV. The combination of D3 and FV has
advantages over D3, FV, and VLAD.

Computer Vision and Pattern Recognition