SHISS: Supervised hashing with informative set selection

In recent years, there has been increasing demand for social security and identity authentication, which leads to the booming of many biometrics involved large-scale problems such as recognition, retrieval, and identification. In this case, traditional models are infeasible due to the limited capability for handling large-scale data. Therefore, hashing technique is becoming prevalent due to its low storage cost and fast query speed. Recently, researchers have shown that supervised hashing can achieve higher accuracy than unsupervised hashing by incorporating tag or label information of data for learning hashing function. However, existing supervised methods treat all training examples equally, ignoring the different impacts of various training examples on the learning process. As a result, their performance is not satisfactory under some practical situations. As an improvement, this paper proposes a new method called “Supervised Hashing with Informative Set Selection” (SHISS), which assumes that different training examples have different influence on the learning process, and their usage should follow a logic way during optimization. In particular, we propose two criteria, certainty and diversity, to evaluate the informativeness of the subsets of training examples and encourage the more informative subsets to be learned ahead of the less informative ones. The experiments on two typical image retrieval datasets and one face dataset demonstrate that the proposed SHISS obtains higher mean average precision and shows faster convergence rate compared with the state-of-the-art hashing methods.