Deep convolutional hashing using pairwise multi-label supervision for large-scale visual search

Image hashing has attracted much attention in the field of large-scale visual search, and learning based approaches have benefited from recent advances of deep learning, which outperforms the shallow models. Most existing deep hashing approaches tend to learn hierarchical models with single-label images limiting the semantic representations. However, few methods have utilized multi-label images to explore rich semantic supervision. In this paper, we propose a new deep convolutional hashing approach by leveraging multi-label images and exploring the label relevance. The proposed method utilizes pairwise supervision to hierarchically transform images into hash codes. Within the deep hashing framework, the Convolutional Neural Networks (CNNs) are considered to automatically learn visual features with smaller semantic gaps. Then a hashing layer using nonlinear mapping is employed to obtain hash codes. A regularized loss function based on pairwise multi-label supervision is proposed to simultaneously learn the features and hash codes. Besides, pairwise multi-label supervision utilizes label relevance to compute semantic similarity of images. The experiments of visual search on two multi-label datasets demonstrate the competitiveness of our proposed approach compared to several state-of-the-art multi-label approaches.