Semi-Paired Asymmetric Deep Cross-Modal Hashing Learning

Abstract

Hashing has been effectively applied in large-scale multimedia retrieval tasks due to the characteristics of fast calculation speed and low storage cost. However, most existing cross-view hashing methods require well paired views, where one sample in one view can always be associated with one sample in another view. Nevertheless, such fully-paired setting is difficult to hold in practice. The cross-view retrieval task for semi-paired data, in which only a portion of the sample is paired, remains more challenging and less explored. In this case, we design an unsupervised deep hashing for large-scale data, named semi-paired asymmetric deep cross-model hashing (SADCH) to address the challenging task. SADCH is a novel asymmetric end-to-end deep neural network model. Specifically, SADCH trains deep network by using query points to improve the training efficiency, and directly learns hash codes of database. A similarity matrix is constructed by a novel cross-view graph to explore the underlying data structures, such that the similarities of paired points and unpaired points can be preserved. As such, the deep features and similarity graph matrix can be jointly used to design an alternating algorithm to efficiently generate more differentiated hash codes. SADCH is evaluated for large-scale cross-view approximate nearest neighbor search on three benchmark datasets and compared against several state-of-the arts. The results of extensive experiments demonstrate the superiority of the proposed SADCH for semi-paired hashing in the unsupervised setting.