  Online image hashing has attracted increasing research attention recently,
which receives large-scale data in a streaming manner to update the hash
functions on-the-fly. Its key challenge lies in the difficulty of balancing the
learning timeliness and model accuracy. To this end, most works follow a
supervised setting, i.e., using class labels to boost the hashing performance,
which defects in two aspects: First, strong constraints, e.g., orthogonal or
similarity preserving, are used, which however are typically relaxed and lead
to large accuracy drop. Second, large amounts of training batches are required
to learn the up-to-date hash functions, which largely increase the learning
complexity. To handle the above challenges, a novel supervised online hashing
scheme termed Hadamard Matrix Guided Online Hashing (HMOH) is proposed in this
paper. Our key innovation lies in introducing Hadamard matrix, which is an
orthogonal binary matrix built via Sylvester method. In particular, to release
the need of strong constraints, we regard each column of Hadamard matrix as the
target code for each class label, which by nature satisfies several desired
properties of hashing codes. To accelerate the online training, LSH is first
adopted to align the lengths of target code and to-be-learned binary code. We
then treat the learning of hash functions as a set of binary classification
problems to fit the assigned target code. Finally, extensive experiments
demonstrate the superior accuracy and efficiency of the proposed method over
various state-of-the-art methods. Codes are available at
https://github.com/lmbxmu/mycode.
