Unsupervised hashing based on the recovery of subspace structures

PATTERN RECOGNITION

卷:103

Article

Tian, Zhibao¹  Zhang, Hui^1,2  Chen, Yong³  Zhang, Dell^4,5 

[1] Beihang Univ, Sch Comp Sci & Engn, Beijing 100191, Peoples R China

[2] Beihang Univ, Beijing Adv Innovat Ctr Big Data & Brain Comp, Beijing 100191, Peoples R China

[3] Peking Univ, Sch Elect Engn & Comp Sci, Beijing 100871, Peoples R China

[4] Birkbeck Univ London, London WC1E 7HX, England

[5] Blue Prism AI Labs, London WC2B 6NH, England

关键词: Semantic hashing;    Subspace learning;    Low-rank representation;    Discrete optimization;   

DOI  :  10.1016/j.patcog.2020.107261

来源: Elsevier

【 摘 要 】

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 sub-problems. 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. (C) 2020 Elsevier Ltd. All rights reserved.

【 授权许可】

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