A fast algorithm for creating a compact and discriminative visual codebook

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.