Global similarity preserving hashing

Hashing learning has attracted increasing attention these years with the explosive increase in data volume. Most existing hashing learning methods can be divided into two stages. Firstly, obtain low-dimensional representation of the original data. Secondly, quantize the low-dimensional representation of each sample and map them to binary codes. This two-stage hashing framework separates projection operation and quantization operation apart, and the original data structure cannot be well preserved after this kind of two-stage operation. Considering this, global similarity preserving hashing (GSPH) is proposed, which utilizes a joint hashing framework to directly project the original data to hamming space, and reduces the projection error and the quantization loss simultaneously. Moreover, GSPH presents a global similarity-based data sample reconstruction method, which describes the intrinsic manifold structure of original data more precisely. The image retrieval experimental results on Corel, CIFAR, LabelMe and NUS-WIDE datasets illustrate that our algorithm outperforms several other state-of-the-art methods.