Attribute hashing for zero-shot image retrieval

Hashing has been recognized as one of the most promising ways in indexing and retrieving high-dimensional data due to the excellent merits in efficiency and effectiveness. Nevertheless, most existing approaches inevitably suffer from the problem of “semantic gap”, especially when facing the rapid evolution of newly-emerging “unseen” categories on the Web. In this work, we propose an innovative approach, termed Attribute Hashing (AH), to facilitate zero-shot image retrieval (i.e., query by “unseen” images). In particular, we propose a multi-layer hierarchy for hashing, which fully exploits attributes to model the relationships among visual features, binary codes and labels. Besides, we deliberately preserve the nature of hash codes (i.e., discreteness and local structure) to the greatest extent. We conduct extensive experiments on several real-world image datasets to show the superiority of our proposed AH approach as compared to the state-of-the-arts.