A fast online spherical hashing method based on data sampling for large scale image retrieval

Abstract Hashing methods are used to perform the approximate nearest neighbor search due to the low storage for binary codes and the fast computation of Hamming distance. However, in most of the hashing methods, the learning process of hash functions has high cost in both time and storage. To overcome this issue, in this paper, a fast online unsupervised hashing method based on data sampling is proposed to learn the hypersphere-based hash functions from the streaming data. By maintaining a small-size data sample to efficiently preserve the properties of the streaming data, the hypersphere-based hash functions are learnt in an online fashion from the data sample and we can justify the hash functions by proving their theoretic properties. To further improve the search accuracy of our method, a new dimensionality reduction algorithm is proposed to learn the projection matrix from the data sample to construct a low-dimensional space. Then, the data sample is projected into the low-dimensional space, and our method can learn the hash functions online from the small-size projected data sample with low computational complexity and storage space. The experiments show that our method has a better search accuracy than other online hashing methods and runs faster in learning the hash functions.