Semi-paired hashing for cross-view retrieval

Hashing techniques have been widely applied in the large-scale cross-view retrieval tasks due to the significant advantage of hash codes in computation and storage efficiency. Most existing cross-view hashing methods can only handle fully-paired scenarios, where all samples from different views are paired. However, such full pairwise correspondences may not be available in practical applications. In this paper, we propose a novel hashing method, named semi-paired hashing (SPH), to deal with a more challenging cross-view retrieval task, where only partial pairwise correspondences are provided in advance. Specifically, SPH aims to preserve within-view similarity and cross-view correlation among multi-view data. Similarity structure within each view is obtained via anchor graph. As limited samples are paired, correlation between unpaired samples is exploited via a simple yet effective approach, which estimates cross-view correlation by partial cross-view pairwise information and within-view similarity structure. Besides, we further incorporate two regression terms between original features and target binary codes to reduce the quantization loss. An efficient iterative algorithm is presented to simultaneously solve hash functions and binary codes. Extensive experiments on two benchmark datasets demonstrate the superiority of SPH over the state-of-the-art methods, especially in the semi-paired scenarios.