  Online hashing methods usually learn the hash functions online, aiming to
efficiently adapt to the data variations in the streaming environment. However,
when the hash functions are updated, the binary codes for the whole database
have to be updated to be consistent with the hash functions, resulting in the
inefficiency in the online image retrieval process. In this paper, we propose a
novel online hashing framework without updating binary codes. In the proposed
framework, the hash functions are fixed and a parametric similarity function
for the binary codes is learnt online to adapt to the streaming data.
Specifically, a parametric similarity function that has a bilinear form is
adopted and a metric learning algorithm is proposed to learn the similarity
function online based on the characteristics of the hashing methods. The
experiments on two multi-label image datasets show that our method is
competitive or outperforms the state-of-the-art online hashing methods in terms
of both accuracy and efficiency for multi-label image retrieval.
