Dual enhanced semantic hashing for fast image retrieval

As a highly promising technique in the field of similarity search, the hashing-based image retrieval algorithm has received continued attention in recent years because of its strong ability to efficiently provide accurate results when measuring similarities between data instances in the binary space. At the level of practical applications, it is necessary to consider both the optimization of the hashing algorithm itself and the mining of image semantic features to produce high-quality hash codes with a strong semantic resolution, enabling the performance maximisation of such a neighbour search system on complex scenario images. To this end, a unified deep hashing framework termed Dual Enhanced Semantic Hashing (DESH) is proposed in this work. Specifically, it benefits the fast image retrieval in two aspects: 1) By taking advantage of dynamic multi-scale fusion and graph encoding networks, the dual enhanced feature learning significantly strengthens the semantic feature representation by jointly exploring and encoding the local multi-scale information with the high-order adjacency relationship between original images; 2) With the joint optimization of diverse loss functions, the binary semantic modelling process seamlessly module the image semantic information within the hash function learning in the code generation, aiming to generate discriminative hash codes to refine the retrieval performance eventually. By conducting extensive experiments on public datasets, the retrieval results further validate the claims of the proposed DESH by exhibiting its superior performance against competitive state-of-the-art baselines.