Learning Multilevel Semantic Similarity for Large-Scale Multi-Label Image Retrieval

We present a novel Deep Supervised Hashing with code operation (DSOH) method for large-scale multi-label image retrieval. This approach is in contrast with existing methods in that we respect both the intention gap and the intrinsic multilevel similarity of multi-labels. Particularly, our method allows a user to simultaneously present multiple query images rather than a single one to better express her intention, and correspondingly a separate sub-network in our architecture is specifically designed to fuse the query intention represented by each single query. Furthermore, as in the training stage, each image is annotated with multiple labels to enrich its semantic representation, we propose a new margin-adaptive triplet loss to learn the fine-grained similarity structure of multi-labels, which is known to be hard to capture. The whole system is trained in an end-to-end manner, and our experimental results demonstrate that the proposed method is not only able to learn useful multilevel semantic similarity-preserving binary codes but also achieves state-of-the-art retrieval performance on three popular datasets.