Unsupervised hashing based on the recovery of subspace structures

Abstract

Unsupervised semantic hashing should in principle keep the semantics among samples consistent with the intrinsic geometric structures of the dataset. In this paper, we propose a novel multiple stage unsupervised hashing method, named “Unsupervised Hashing based on the Recovery of Subspace Structures” (RSSH) for image retrieval. Specifically, we firstly adapt the Low-rank Representation (LRR) model into a new variant which treats the real-world data as samples drawn from a union of several low-rank subspaces. Then, the pairwise similarities are represented in a space-and-time saving manner based on the learned low-rank correlation matrix of the modified LRR. Next, the challenging discrete graph hashing is employed for binary hashing codes. Notably, we convert the original graph hashing model into an optimization-friendly formalization, which is addressed with efficient closed-form solutions for its subproblems. Finally, the devised linear hash functions are fast achieved for out-of-samples. Retrieval experiments on four image datasets testify the superiority of RSSH to several state-of-the-art hashing models. Besides, it’s worth mentioning that RSSH, a shallow model, significantly outperforms two recently proposed unsupervised deep hashing methods, which further confirms its effectiveness.