Unsupervised Rank-Preserving Hashing for Large-Scale Image Retrieval.

We propose an unsupervised hashing method, exploiting a shallow neural network, that aims to produce binary codes that preserve the ranking induced by an original real-valued representation. This is motivated by the emergence of small-world graph-based approximate search methods that rely on local neighborhood ranking. We formalize the training process in an intuitive way by considering each training sample as a query and aiming to obtain a ranking of a random subset of the training set using the hash codes that is the same as the ranking using the original features. We also explore the use of a decoder to obtain an approximated reconstruction of the original features. At test time, we retrieve the most promising database samples using only the hash codes and perform re-ranking using the reconstructed features, thus allowing the complete elimination of the original real-valued features and the associated high memory cost. Experiments conducted on publicly available large-scale datasets show that our method consistently outperforms all compared state-of-the-art unsupervised hashing methods and that the reconstruction procedure can effectively boost the search accuracy with a minimal constant additional cost.