Online Supervised Sketching Hashing for Large-Scale Image Retrieval

Abstract

Online hashing methods have achieved a good tradeoff between the accuracy and the efficiency for learning the hash functions in the online settings. Compared to the stochastic gradient descent-based online hashing methods, the data sketching-based online hashing methods can preserve more information from the streaming data to learn the hash functions. However, the existing data sketching-based methods are unsupervised hashing methods which cannot utilize the supervised information to alleviate the gap between the binary codes and the semantic similarity. In this paper, we propose an online supervised hashing method in which a semantic similarity graph is designed to describe the data similarity according to the supervised information, and a data sketch is developed to preserve the characteristics of the graph with significantly smaller size in the online settings. Hence, the hash functions can be learned online from the data sketch with low computational time cost and small space storage. Furthermore, we develop a parallel computing version of the proposed method, aiming at accelerating the process of learning the hash functions. The experiments on three large-scale datasets show that our method can achieve comparable or better search accuracy compared to other online hashing methods.