Abstract
Online hashing methods aim to learn compact binary codes of the new data stream, and update the hash function to renew the codes of the existing data. However, the addition of new data streams has a vital impact on the retrieval performance of the entire retrieval system, especially the similarity measurement between new data streams and existing data, which has always been one of the focuses of online retrieval research. In this paper, we present a novel scalable supervised online hashing method, to solve the above problems within a unified framework. Specifically, the similarity matrix is established by the label matrix of the existing data and the new data stream. The projection of the existing data label matrix is then used as an intermediate term to approximate the binary codes of the existing data, which not only realizes the semantic information of the hash codes learning but also effectively alleviates the problem of data imbalance. In addition, an alternate optimization algorithm is proposed to efficiently make the solution of the model. Extensive experiments on three widely used datasets validate its superior performance over several state-of-the-art methods in terms of both accuracy and scalability for online retrieval task.
Deep Learning to Ternary Hash Codes by Continuation