Towards Discriminative Visual Search via Semantically Cycle-consistent Hashing Networks

Deep hashing has shown great potentials in large-scale visual similarity search due to preferable storage and computation efficiency. Typically, deep hashing encodes visual features into compact binary codes by preserving representative semantic visual features. Works in this area mainly focus on building the relationship between the visual and objective hash space, while they seldom study the triadic cross-domain semantic knowledge transfer among visual, semantic and hashing spaces, leading to serious semantic ignorance problem during space transformation. In this paper, we propose a novel deep tripartite semantically interactive hashing framework, dubbed Semantically Cycle-consistent Hashing Networks (SCHN), for discriminative hash code learning. Particularly, we construct a flexible semantic space and a transitive latent space, in conjunction with the visual space, to jointly deduce the privileged discriminative hash space. Specifically, a semantic space is conceived to strengthen the flexibility and completeness of categories in feature inference. Moreover, a transitive latent space is formulated to explore the shared semantic interactivity embedded in visual and semantic features. Our SCHN, for the first time, establishes the cyclic principle of deep semantic-preserving hashing by adaptive semantic parsing across different spaces in visual similarity search. In addition, the entire learning framework is jointly optimized in an end-to-end manner. Extensive experiments performed on diverse large-scale datasets evidence the superiority of our method against other state-of-the-art deep hashing algorithms.