  We propose an incremental strategy for learning hash functions with kernels
for large-scale image search. Our method is based on a two-stage classification
framework that treats binary codes as intermediate variables between the
feature space and the semantic space. In the first stage of classification,
binary codes are considered as class labels by a set of binary SVMs; each
corresponds to one bit. In the second stage, binary codes become the input
space of a multi-class SVM. Hash functions are learned by an efficient
algorithm where the NP-hard problem of finding optimal binary codes is solved
via cyclic coordinate descent and SVMs are trained in a parallelized
incremental manner. For modifications like adding images from a previously
unseen class, we describe an incremental procedure for effective and efficient
updates to the previous hash functions. Experiments on three large-scale image
datasets demonstrate the effectiveness of the proposed hashing method,
Supervised Incremental Hashing (SIH), over the state-of-the-art supervised
hashing methods.
