Topic Hypergraph Hashing for Mobile Image Retrieval

Abstract

Hashing is one of the promising solutions to support efficient Mobile Image Retrieval (MIR). However, most of existing hashing strategies simply rely on low-level features, which inevitably makes the generated hashing codes less semantic. Moreover, many of them fail to exploit complex and high-order semantic correlations of images. Motivated by these observations, we propose a novel unsupervised hashing scheme, \emph{Topic Hypergraph Hashing} (THH), to address the limitations. A unified topic hypergraph, where images and topics are represented with independent vertices and hyperedges respectively, is first constructed to model latent semantics of images and their correlations. With topic hypergraph model, hashing codes and functions are then learned by simultaneously preserving similarity consistence and semantic correlation. Experiments on standard datasets demonstrate that THH can achieve superior performance compared with several state-of-the-art techniques, and it is more suitable for MIR.