Unsupervised hashing based on the recovery of subspace structures. (July 2020)

• Record Type: Journal Article
• Authors: Tian, Zhibao; Zhang, Hui; Chen, Yong; Zhang, Dell
• Is Part Of: Pattern recognition. Volume 103(2020:Jul.)
• Publication Date: 2020-07
• Subjects: Semantic hashing -- Subspace learning -- Low-rank representation -- Discrete optimization; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2020.107261
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: Our method adapts the LRR model into a new variant, based on which the learned correlation matrix could be designed into a space-and-time saving formula for data semantics. To tackle the discrete graph hashing, we presents a new learning method, i.e., transforms the original optimization problem into three subproblems by means of surrogate variables, and most importantly each subproblem is addressed with a closed-form solution, which makes the whole hashing learning converge within dozens of iterations. Experiments on four datasets demonstrate the advantages of our method over several state-of- the-art unsupervised hashing models including two recently proposed unsupervised deep hashing methods. 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.