  Online hashing has attracted extensive research attention when facing
streaming data. Most online hashing methods, learning binary codes based on
pairwise similarities of training instances, fail to capture the semantic
relationship, and suffer from a poor generalization in large-scale applications
due to large variations. In this paper, we propose to model the similarity
distributions between the input data and the hashing codes, upon which a novel
supervised online hashing method, dubbed as Similarity Distribution based
Online Hashing (SDOH), is proposed, to keep the intrinsic semantic relationship
in the produced Hamming space. Specifically, we first transform the discrete
similarity matrix into a probability matrix via a Gaussian-based normalization
to address the extremely imbalanced distribution issue. And then, we introduce
a scaling Student t-distribution to solve the challenging initialization
problem, and efficiently bridge the gap between the known and unknown
distributions. Lastly, we align the two distributions via minimizing the
Kullback-Leibler divergence (KL-diverence) with stochastic gradient descent
(SGD), by which an intuitive similarity constraint is imposed to update hashing
model on the new streaming data with a powerful generalizing ability to the
past data. Extensive experiments on three widely-used benchmarks validate the
superiority of the proposed SDOH over the state-of-the-art methods in the
online retrieval task.
