Semantic Augmentation Hashing For Zero-Shot Image Retrieval

Hashing technique has been widely applied to large-scale image retrieval due to its efficacy in storage and retrieval. However, due to the explosive growth of multimedia data on the web, existing hashing approaches can hardly achieve satisfactory performance on the newly-emerging images of new classes. In this paper, we propose a novel Semantic Augmentation Hashing (SAH) for zero-shot image retrieval. The class semantic embeddings are used as an intermediate space between visual features and binary codes to align visual features to corresponding class semantics and to transfer knowledge from seen classes to unseen classes simultaneously. Extensive experiments conducted on two datasets with different scales demonstrate the superiority of our method as compared against the state-of-the-arts.