In patch-based object recognition, using a compact visual
codebook can boost computational efficiency and reduce memory cost.
Nevertheless, compared with a large-sized codebook, it also risks the loss
of discriminative power. Moreover, creating a compact visual codebook
can be very time-consuming, especially when the number of initial visual
words is large. In this paper, to minimize its loss of discriminative power,
we propose an approach to build a compact visual codebook by maximally
preserving the separability of the object classes. Furthermore, a
fast algorithm is designed to accomplish this task effortlessly, which can
hierarchically merge 10,000 visual words down to 2 in ninety seconds.
Experimental study shows that the compact visual codebook created in
this way can achieve excellent classification performance even after a
considerable reduction in size.